Association of Combined Anti-Ro52/TRIM21 and Anti-Ro60/SSA Antibodies With Increased Sjögren Disease Severity Through Interferon Pathway Activation

OBJECTIVE

The biologic diagnosis of primary Sjögren disease (SjD) mainly relies on anti-Ro60/SSA antibodies, whereas the significance of anti-Ro52/TRIM21 antibodies currently remains unclear. The aim of this study was to characterize the clinical, serological, biologic, transcriptomic, and interferon profiles of patients with SjD according to their anti-Ro52/TRIM21 antibody status.

METHODS

Patients with SjD from the European PRECISESADS (n = 376) and the Brittany Diagnostic Suspicion of primitive Sjögren's Syndrome (DIApSS); (n = 146) cohorts were divided into four groups: double negative (Ro52-/Ro60-), isolated anti-Ro52/TRIM21 positive (Ro52+), isolated anti-Ro60/SSA positive (Ro60+), and double-positive (Ro52+/Ro60+) patients. Clinical information; EULAR Sjögren Syndrome Disease Activity Index, a score representing systemic activity; and biologic markers associated with disease severity were evaluated. Transcriptome data obtained from whole blood by RNA sequencing and type I and II interferon signatures were analyzed for PRECISESADS patients.

RESULTS

In the DIApSS cohort, Ro52+/Ro60+ patients showed significantly more parotidomegaly (33.3% vs 0%-11%) along with higher β2-microglobulin (P = 0.0002), total immunoglobulin (P < 0.0001), and erythrocyte sedimentation rate levels (P = 0.002) as well as rheumatoid factor (RF) positivity (66.2% vs 20.8%-25%) compared to other groups. The PRECISESADS cohort corroborated these observations, with increased arthritis (P = 0.046), inflammation (P = 0.005), hypergammaglobulinemia (P < 0.0001), positive RF (P < 0.0001), leukopenia (P = 0.004), and lymphopenia (P = 0.009) in Ro52+/Ro60+ patients. Cumulative EULAR Sjögren Syndrome Disease Activity Index results further confirmed these disparities (P = 0.002). Transcriptome analysis linked anti-Ro52/TRIM21 antibody positivity to interferon pathway activation as an underlying cause for these clinical correlations.

CONCLUSION

These results suggest that the combination of anti-Ro52/TRIM21 and anti-Ro60/SSA antibodies is associated with a clinical, biologic, and transcriptional profile linked to greater disease severity in SjD through the potentiation of the interferon pathway activation by anti-Ro52/TRIM21 antibodies.

Recommendations of the French Society of Rheumatology for the management in current practice of patients with Polymyalgia Rheumatica

OBJECTIVE

To develop recommendations for the routine management of patients with polymyalgia rheumatica (PMR) Methods: Following standard procedures, a systematic review of the literature by 5 supervised junior rheumatologists, based on the questions selected by the steering committee (5 senior rheumatologists), was used as the basis for working meetings, followed by a one-day plenary meeting with the working group (15 members), leading to the development of the wording and determination of the strength of the recommendations and the level of agreement of the experts.

RESULTS

Five general principles and 19 recommendations were drawn up. Three recommendations relate to diagnosis and the use of imaging, and 5 to the assessment of the disease, its activity and co-morbidities. Non-pharmacological therapies are the subject of one recommendation. Three recommendations concern initial treatment based on general corticosteroid therapy, 5 concern the reduction of corticosteroid therapy and follow-up, and 2 concern corticosteroid dependence and steroid-sparing treatments (anti-IL-6).

CONCLUSION

These recommendations take account of current data on PMR, with the aim of reducing exposure to corticosteroid therapy and its side effects in a fragile population. They are intended to be practical, to help practitioners in the day-to-day management of patients with PMR.

The Sjögren's Working Group: The 2023 OMERACT meeting and provisional domain generation

Sjögren's disease (SjD) is a systemic autoimmune exocrinopathy with key features of dryness, pain, and fatigue. SjD can affect any organ system with a variety of presentations across individuals. This heterogeneity is one of the major barriers for developing effective disease modifying treatments. Defining core disease domains comprising both specific clinical features and incorporating the patient experience is a critical first step to define this complex disease. The OMERACT SjD Working Group held its first international collaborative hybrid meeting in 2023, applying the OMERACT 2.2 filter toward identification of core domains. We accomplished our first goal, a scoping literature review that was presented at the Special Interest Group held in May 2023. Building on the domains identified in the scoping review, we uniquely deployed multidisciplinary experts as part of our collaborative team to generate a provisional domain list that captures SjD heterogeneity.

Identification of outcome domains in primary Sjögren's disease: A scoping review by the OMERACT Sjögren disease working group

OBJECTIVES

Sjögren's disease (SjD) is a heterogenous disease with a wide range of manifestations, ranging from symptoms of dryness, fatigue, and pain, to systemic involvement. Considerable advances have been made to evaluate systemic activity or patient-reported outcomes, but most of the instruments were not able to assess all domains of this multifaceted disease. The aim of this scoping review was to generate domains that have been assessed in randomized controlled trials, as the first phase of the Outcome Measures in Rheumatology (OMERACT) process of core domain set development.

METHODS

We systematically searched Medline (Pubmed) and EMBASE between 2002 and March 2023 to identify all randomized controlled trials assessing relevant domains, using both a manual approach and an artificial intelligence software (BIBOT) that applies natural language processing to automatically identify relevant abstracts. Domains were mapped to core areas, as suggested by the OMERACT 2.1 Filter.

RESULTS

Among the 5,420 references, we included 60 randomized controlled trials, focusing either on overall disease manifestations (53%) or on a single organ/symptom: dry eyes (17%), xerostomia (15%), fatigue (12%), or pulmonary function (3%). The most frequently assessed domains were perceived dryness (52% for overall dryness), fatigue (57%), pain (52%), systemic disease activity (45%), lacrimal gland function (47%) and salivary function (55%), B-cell activation (60%), and health-related quality of life (40%).

CONCLUSION

Our scoping review highlighted the heterogeneity of SjD, in the study designs and domains. This will inform the OMERACT SjD working group to select the most appropriate core domains to be used in SjD clinical trials and to guide the future agenda for outcome measure research in SjD.

YOUPI: Your powerful and intelligent tool for segmenting cells from imaging mass cytometry data

The recent emergence of imaging mass cytometry technology has led to the generation of an increasing amount of high-dimensional data and, with it, the need for suitable performant bioinformatics tools dedicated to specific multiparametric studies. The first and most important step in treating the acquired images is the ability to perform highly efficient cell segmentation for subsequent analyses. In this context, we developed YOUPI (Your Powerful and Intelligent tool) software. It combines advanced segmentation techniques based on deep learning algorithms with a friendly graphical user interface for non-bioinformatics users. In this article, we present the segmentation algorithm developed for YOUPI. We have set a benchmark with mathematics-based segmentation approaches to estimate its robustness in segmenting different tissue biopsies.

Machine Learning for the Identification of a Common Signature for Anti-SSA/Ro 60 Antibody Expression Across Autoimmune Diseases

OBJECTIVE

Anti-Ro autoantibodies are among the most frequently detected extractable nuclear antigen autoantibodies, mainly associated with primary Sjögren's syndrome (SS), systemic lupus erythematosus (SLE), and undifferentiated connective tissue disease (UCTD). This study was undertaken to determine if there is a common signature for all patients expressing anti-Ro 60 autoantibodies regardless of their disease phenotype.

METHODS

Using high-throughput multiomics data collected from the cross-sectional cohort in the PRECISE Systemic Autoimmune Diseases (PRECISESADS) study Innovative Medicines Initiative (IMI) project (genetic, epigenomic, and transcriptomic data, combined with flow cytometry data, multiplexed cytokines, classic serology, and clinical data), we used machine learning to assess the integrated molecular profiling of 520 anti-Ro 60+ patients compared to 511 anti-Ro 60- patients with primary SS, patients with SLE, and patients with UCTD, and 279 healthy controls.

RESULTS

The selected clinical features for RNA-Seq, DNA methylation, and genome-wide association study data allowed for a clear distinction between anti-Ro 60+ and anti-Ro 60- patients. The different features selected using machine learning from the anti-Ro 60+ patients constituted specific signatures when compared to anti-Ro 60- patients and healthy controls. Remarkably, the transcript Z score of 3 genes (ATP10A, MX1, and PARP14), presenting with overexpression associated with hypomethylation and genetic variation and independently identified using the Boruta algorithm, was clearly higher in anti-Ro 60+ patients compared to anti-Ro 60- patients regardless of disease type. Our findings demonstrated that these signatures, enriched in interferon-stimulated genes, were also found in anti-Ro 60+ patients with rheumatoid arthritis and those with systemic sclerosis and remained stable over time and were not affected by treatment.

CONCLUSION

Anti-Ro 60+ patients present with a specific inflammatory signature regardless of their disease type, suggesting that a dual therapeutic approach targeting both Ro-associated RNAs and anti-Ro 60 autoantibodies should be considered.

OP0009 MACHINE LEARNING IDENTIFIES A COMMON SIGNATURE FOR ANTI-SSA/Ro60 Antibody Expression Across Autoimmune Diseases

Background

Anti-SSA/Ro autoantibodies are among the most frequently detected extractable nuclear antigen autoantibodies and have mainly been associated with primary Sjögren’s syndrome (pSS), systemic lupus erythematosus (SLE) and undifferentiated connective tissue disease (UCTD).

Objectives

Is there a common signature to all patients expressing anti-Ro60 autoantibodies regardless of their disease phenotype?

Methods

Using high-throughput multi-omics data collected within the cross-sectional cohort from the PRECISESADS IMI project [1] (genetic, epigenomic, transcriptomic, combined with flow cytometric data, multiplexed cytokines, classical serology and clinical data), we assessed by machine learning the integrated molecular profiling of 520 anti-Ro60-positive (anti-Ro60+) compared to 511 anti-Ro60-negative (anti-Ro60-) patients with pSS, SLE and UCTD, and 279 healthy controls (HCs).

Results

The selected features for RNA-Seq, DNA methylation and GWAS data allowed a clear separation between anti-Ro60+ and anti-Ro60- patients. These results demonstrate that the different features selected by machine learning from the anti-Ro60+ patients constitute specific signatures when compared to anti-Ro60- patients and HCs. Remarkably, the gene transcript z-score of three genes (ATP10A, MX1 and PARP14), presenting an overexpression associated with a hypomethylation and genetic variation, and independently identified by the Boruta algorithm, was clearly higher in anti-Ro60+ patients compared to anti-Ro60- patients in all the diseases (Figure 1). Finally, we demonstrate that these signatures, enriched in interferon stimulated genes, were also found in anti-Ro60+ patients with rheumatoid arthritis and systemic sclerosis.

Figure 1.

Three genes common to RNA-Seq, DNA methylation and GWAS analysis characterize anti-Ro60+ patients. (A) ATP10/MX1/PARP14 z-score analyses were performed for 731 patients and 254 HCs according to anti-Ro60 expression. (B) ATP10/MX1/PARP14 z-score analyses were performed for 286 pSS, 351 SLE and 94 UCTD patients and 254 HCs. Two-tailed pairwise Wilcoxon-rank sum test results are shown. Plots show median, with error bars indicating ± interquartile range. (pSS: primary Sjögren’s syndrome, SLE: systemic lupus erythematosus, UCTD: undifferentiated connective tissue disease, HCs: healthy controls).

Conclusion

Anti-Ro60+ patients present a specific inflammatory signature regardless of their disease suggesting that a dual approach targeting both Ro-associated RNAs and anti-Ro60 autoantibodies should be considered.

References

[1]Barturen G, Babaei S, Català-Moll F, Martínez-Bueno M, Makowska Z, Martorell-Marugán J, et al. Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases. Arthritis Rheumatol Hoboken NJ. 2021 Jun;73(6):1073–85

Funding

The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under the Grant Agreement Number 115565 (PRECISESADS project), resources of which are composed of financial contribution from the European Union’s Seventh Framework Program (FP7/2007–2013) and EFPIA companies’ in-kind contribution.

Acknowledgements

The study has been conducted thank to the contribution of the PRECISESADS clinical consortium and the PRECISESADS flow cytometry consortium.

Disclosure of Interests

None declared.

Towards a universal definition of disease activity score thresholds: the AS135 score

OBJECTIVES

Many study groups have developed scores to reflect disease activity. The result of this fragmented process is a multitude of disease activity scores, even for a single disease. We aimed to identify and standardise disease activity scores in rheumatologyMETHODS: We conducted a literature review on disease activity criteria using both a manual approach and in-house computer software (BIBOT) that applies natural language processing to automatically identify and interpret important words in abstracts published in English between 1.1.1975 and 31.12.2018. We selected activity scores with cut-off values divided into four classes (remission and low, moderate and high disease activity). We used a linear interpolation to map disease activity scores to our new score, the AS135, and developed a smartphone application to perform the conversion.

RESULTS

A total of 108 activity criteria from various fields were identified, but it was in rheumatology that we found the most pronounced separation into four classes. We built the AS135 score modification for each selected score using a linear interpolation of the existing criteria. The score modification was defined on the interval [0,10], and values of 1, 3 and 5 were used as thresholds. These arbitrary thresholds were then associated with the thresholds of the existing criteria, and an interpolation was calculated, allowing conversion of the existing criteria into the AS135 criterion. Finally, we created a mobile application.

CONCLUSIONS

We developed an application for clinicians that enables the use of a single disease activity score for different inflammatory rheumatic diseases using an intuitive scale.

A new molecular classification to drive precision treatment strategies in primary Sjögren's syndrome

There is currently no approved treatment for primary Sjögren's syndrome, a disease that primarily affects adult women. The difficulty in developing effective therapies is -in part- because of the heterogeneity in the clinical manifestation and pathophysiology of the disease. Finding common molecular signatures among patient subgroups could improve our understanding of disease etiology, and facilitate the development of targeted therapeutics. Here, we report, in a cross-sectional cohort, a molecular classification scheme for Sjögren's syndrome patients based on the multi-omic profiling of whole blood samples from a European cohort of over 300 patients, and a similar number of age and gender-matched healthy volunteers. Using transcriptomic, genomic, epigenetic, cytokine expression and flow cytometry data, combined with clinical parameters, we identify four groups of patients with distinct patterns of immune dysregulation. The biomarkers we identify can be used by machine learning classifiers to sort future patients into subgroups, allowing the re-evaluation of response to treatments in clinical trials.

New criteria and new methodological tools for devising criteria sets of inflammatory rheumatic diseases

Rheumatologists use classification criteria to separate patients with inflammatory rheumatic diseases (IRD). They change over time, and the concepts of the diseases also change. The paradigm is currently moving as the goal of classification in the future will be more to select which patients may be relevant for a specific treatment rather than to describe their characteristics. Therefore, the challenge will be to reclassify multifactorial diseases on the basis of their biological mechanisms rather than their clinical phenotype. Currently, various projects are trying to reclassify diseases using bioinformatics approaches and in the near future the use of advanced machine learning algorithms with large omics datasets could lead to new classification models not only based on a clinical phenotype but also on complex biological profile and common sensitivity to targeted treatment. These models would highlight common biological pathways between patients classified in the same cluster and provide a deep understanding of the mechanisms involved in the patient's clinical phenotype. Such approaches would ultimately lead to classification models that rely more on biological causes than on symptoms. This overview on current classification of subgroups of IRD summarises the classification criteria that we use routinely, and how we will classify IRD in the future using bioinformatics and artificial intelligence techniques.

THU0617-HPR TOWARDS A UNIVERSAL DEFINITION OF DISEASE ACTIVITY SCORES THRESHOLDS

Background:

For rheumatologists monitoring patients with various diseases and dealing with multiple scores with different maximum values (9 for RA-DAS, 6.4 for AS-DAS and 60 for PMR-AS) and values thresholds to characterize the different levels of disease activity (low, intermediate and high) can be a tedious task. The same problematic could arise in other specialty than rheumatology. Normalization of these scores seems to be necessary to facilitate daily clinical practice (1).

Objectives:

To indentify and standardize scores of activity of inflammatory diseases.

Methods:

We conducted a literature review on activity criteria using both a manual approach and the BIBOT software (2) published in English between 1.1.1975 and 31.12.2018. Within all extracted disease activity scores, we selected those with cut off values in four classes (remission, low, moderate and high disease activity). We used a linear interpolation to map all these disease activity scores to our new score, the AS-135, and developed a smart-phone application to perform the conversion automatically.

Results:

1068 articles were analyzed by BIBOT, 86 were excluded on the basis of the language used for their writing and 11 were excluded on the basis of their publication date. 599 were selected based on their titles, abstracts and keywords. 108 activity criteria from various fields (rheumatology, dermatology, gastroenterology, psychiatry, neurology and pneumology) were identified, but it is in rheumatology that we find separation into four classes. 10 scores met our inclusion criteria and were implemented in the Android app. These are: DAS28 (ESR), DAS28 (CRP), SDAI, ASDAS (ESR), ASDAS (CRP), ESSDAI, SLEDAI-2K, DAPSA, PMR-AS (ESR) and PMR-AS (CRP). We built the AS135 score modification for each selected score using a linear interpolation of the existing criteria. It was defined on the interval [0,10] and values 1, 3 and 5 were used as thresholds. These arbitrary thresholds are then associated with the thresholds of the existing criteria and an interpolation can be calculated, allowing the conversion of the existing criteria into AS135 criterion. We have finally created a mobile application that allows each user to obtain both the original value of the activity criterion.

Conclusion:

We have created a mobile application that allows any user to obtain in a simple way the level of disease activity, whatever the criterion used to describe it, since the application returns, in addition to the value of the activity criterion calculated from data returned by the physician, the transformation of this value into AS135 criterion and its interpretation in terms of level of activity of the pathology. The application is now available for Android devices and we plan to start developing a version for iOS devices.

References:

[1]Saraux L, Devauchelle-Pensec V, Saraux A. Plea for standardization of disease activity scores. Rheumatol Oxf Engl. 2019 Aug 1;58(8):1500–1

[2]Orgeolet L, Foulquier N, Misery L, Redou P, Pers J-O, Devauchelle-Pensec V, et al. Can artificial intelligence replace manual search for systematic literature? Review on cutaneous manifestations in primary Sjögren’s syndrome. Rheumatol Oxf Engl. 2019 Aug 31;

Disclosure of Interests:

None declared

Can artificial intelligence replace manual search for systematic literature? Review on cutaneous manifestations in primary Sjögren’s syndrome

Objectives

Manual systematic literature reviews are becoming increasingly challenging due to the sharp rise in publications. The primary objective of this literature review was to compare manual and computer software using artificial intelligence retrieval of publications on the cutaneous manifestations of primary SS, but we also evaluated the prevalence of cutaneous manifestations in primary SS.

Methods

We compared manual searching and searching with the in-house computer software BIbliography BOT (BIBOT) designed for article retrieval and analysis. Both methods were used for a systematic literature review on a complex topic, i.e. the cutaneous manifestations of primary SS. Reproducibility was estimated by computing Cohen’s κ coefficients and was interpreted as follows: slight, 0–0.20; fair, 0.21–0.40; moderate, 0.41–0.60; substantial, 0.61–0.80; and almost perfect, 0.81–1.

Results

The manual search retrieved 855 articles and BIBOT 1042 articles. In all, 202 articles were then selected by applying exclusion criteria. Among them, 155 were retrieved by both methods, 33 by manual search only, and 14 by BIBOT only. Reliability (κ = 0.84) was almost perfect. Further selection was performed by reading the 202 articles. Cohort sizes and the nature and prevalence of cutaneous manifestations varied across publications. In all, we found 52 cutaneous manifestations reported in primary SS patients. The most described ones were cutaneous vasculitis (561 patients), xerosis (651 patients) and annular erythema (215 patients).

Conclusion

Among the final selection of 202 articles, 155/202 (77%) were found by the two methods but BIBOT was faster and automatically classified the articles in a chart. Combining the two methods retrieved the largest number of publications.

Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review

Big data analysis has become a common way to extract information from complex and large datasets among most scientific domains. This approach is now used to study large cohorts of patients in medicine. This work is a review of publications that have used artificial intelligence and advanced machine learning techniques to study physio pathogenesis-based treatments in pSS. A systematic literature review retrieved all articles reporting on the use of advanced statistical analysis applied to the study of systemic autoimmune diseases (SADs) over the last decade. An automatic bibliography screening method has been developed to perform this task. The program called BIBOT was designed to fetch and analyze articles from the pubmed database using a list of keywords and Natural Language Processing approaches. The evolution of trends in statistical approaches, sizes of cohorts and number of publications over this period were also computed in the process. In all, 44077 abstracts were screened and 1017 publications were analyzed. The mean number of selected articles was 101.0 (S.D. 19.16) by year, but increased significantly over the time (from 74 articles in 2008 to 138 in 2017). Among them only 12 focused on pSS but none of them emphasized on the aspect of pathogenesis-based treatments. To conclude, medicine progressively enters the era of big data analysis and artificial intelligence, but these approaches are not yet used to describe pSS-specific pathogenesis-based treatment. Nevertheless, large multicentre studies are investigating this aspect with advanced algorithmic tools on large cohorts of SADs patients.