Machine learning integration of scleroderma histology and gene expression identifies fibroblast polarisation as a hallmark of clinical severity and improvement

Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives

Autoimmune rheumatic diseases (ARD) present a significant global health challenge characterized by a rising prevalence. These highly heterogeneous diseases involve complex pathophysiological mechanisms, leading to variable treatment efficacies across individuals. This variability underscores the need for personalized and precise treatment strategies. Traditionally, clinical practices have depended on empirical treatment selection, which often results in delays in effective disease management and can cause irreversible damage to multiple organs. Such delays significantly affect patient quality of life and prognosis. Artificial intelligence (AI) has recently emerged as a transformative tool in rheumatology, offering new insights and methodologies. Current research explores AI's capabilities in diagnosing diseases, stratifying risks, assessing prognoses, and predicting treatment responses in ARD. These developments in AI offer the potential for more precise and targeted treatment strategies, fostering optimism for enhanced patient outcomes. This paper critically reviews the latest AI advancements for predicting treatment responses in ARD, highlights the current state of the art, identifies ongoing challenges, and proposes directions for future research. By capitalizing on AI's capabilities, researchers and clinicians are poised to develop more personalized and effective interventions, improving care and outcomes for patients with ARD.

Can machine learning assist in systemic sclerosis diagnosis and management? A scoping review

This scoping review aims to summarize the existing literature on how machine learning can be used to impact systemic sclerosis diagnosis, management, and treatment. Following Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) reporting guidelines, Embase, Web of Science, Medline (PubMed), IEEE Xplore, and ACM Digital Library were searched from inception to 3 March 2024, for primary literature reporting on machine learning models in any capacity regarding scleroderma. Following robust triaging, 11 retrospective studies were included in this scoping review. Three studies focused on the diagnosis of scleroderma to influence preferred management and nine studies on treatment and predicting treatment response to scleroderma. Nine studies used supervision in their machine learning model training; two used supervised and unsupervised training and one used solely unsupervised training. A total of 817 patients were included in the data sets. Seven of the included articles used patients from the United States, one from Belgium, two from Japan, and two from China. Although currently limited to retrospective studies, the results indicate that machine learning modeling may have a role in early diagnosis, management, therapeutic decision-making, and in the development of future therapies for systemic sclerosis. Prospective studies examining the use of machine learning in clinical practice are recommended to confirm the utility of machine learning in patients with systemic sclerosis.

Is cyclophosphamide still the gold standard in early severe rapidly progressive systemic sclerosis?

Cyclophosphamide (CYC) has been a gold standard of treatment for severe progressive Systemic Sclerosis (SSc), especially in patients with concomitant interstitial lung disease (ILD). This approach was based on results of several interventional studies, including randomized control trials, which mainly addressed SSc-ILD as a primary end point and skin involvement as a second one. The use of CYC is time-limited due to significant adverse events. More recently, other immunosuppressive and biological agents showed efficacy but better safety profile in patients with SSc and SSc-ILD. With regards to other end-points, post-hoc analyses, systematic reviews and metalysis showed that CYC had limited influence on patients' quality of life, event-free survival and mortality. Comprehensive patient's stratification according to a molecular, cellular and phenotypic pattern may help in choosing of personalized medicine with more ambitious treatment effect and should be the future direction. According to the above available data and even if scientific evidence may be missing, experts' opinion has changed the attitude to CYC as an anchor drug in the management of severe SSc. Indeed, CYC has been pushed to the second and even third treatment option after mycophenolate mofetil, tocilizumab or rituximab. This position became obvious during debate on this topic at CORA meeting 2023.

Skin Gene Expression Profiles in Systemic Sclerosis: From Clinical Stratification to Precision Medicine

Systemic sclerosis, also known as scleroderma or SSc, is a condition characterized by significant heterogeneity in clinical presentation, disease progression, and response to treatment. Consequently, the design of clinical trials to successfully identify effective therapeutic interventions poses a major challenge. Recent advancements in skin molecular profiling technologies and stratification techniques have enabled the identification of patient subgroups that may be relevant for personalized treatment approaches. This narrative review aims at providing an overview of the current status of skin gene expression analysis using computational biology approaches and highlights the benefits of stratifying patients upon their skin gene signatures. Such stratification has the potential to lead toward a precision medicine approach in the management of SSc.

Extensive and Persistent Extravascular Dermal Fibrin Deposition Characterizes Systemic Sclerosis

Systemic sclerosis (SSc) is an autoimmune disease characterized by progressive multiorgan fibrosis. While the cause of SSc remains unknown, a perturbed vasculature is considered a critical early step in the pathogenesis. Using fibrinogen as a marker of vascular leakage, we found extensive extravascular fibrinogen deposition in the dermis of both limited and diffuse systemic sclerosis disease, and it was present in both early and late-stage patients. Based on a timed series of excision wounds, retention on the fibrin deposit of the splice variant domain, fibrinogen αEC, indicated a recent event, while fibrin networks lacking the αEC domain were older. Application of this timing tool to SSc revealed considerable heterogeneity in αEC domain distribution providing unique insight into disease activity. Intriguingly, the fibrinogen-αEC domain also accumulated in macrophages. These observations indicate that systemic sclerosis is characterized by ongoing vascular leakage resulting in extensive interstitial fibrin deposition that is either continually replenished and/or there is impaired fibrin clearance. Unresolved fibrin deposition might then incite chronic tissue remodeling.

Single-cell sequencing shows cellular heterogeneity of cutaneous lesions in lupus erythematosus

Discoid lupus erythematosus (DLE) and systemic lupus erythematosus (SLE) are both types of lupus, yet the characteristics, and differences between them are not fully understood. Here we show single-cell RNA sequencing data of cutaneous lesions from DLE and SLE patients and skin tissues from healthy controls (HCs). We find significantly higher proportions of T cells, B cells and NK cells in DLE than in SLE. Expanded CCL20+ keratinocyte, CXCL1+ fibroblast, ISGhiCD4/CD8 T cell, ISGhi plasma cell, pDC, and NK subclusters are identified in DLE and SLE compared to HC. In addition, we observe higher cell communication scores between cell types such as fibroblasts and macrophage/dendritic cells in cutaneous lesions of DLE and SLE compared to HC. In summary, we clarify the heterogeneous characteristics in cutaneous lesions between DLE and SLE, and discover some specific cell subtypes and ligand-receptor pairs that indicate possible therapeutic targets of lupus erythematosus.

Wound healing, fibroblast heterogeneity, and fibrosis

Fibroblasts are highly dynamic cells that play a central role in tissue repair and fibrosis. However, the mechanisms by which they contribute to both physiologic and pathologic states of extracellular matrix deposition and remodeling are just starting to be understood. In this review article, we discuss the current state of knowledge in fibroblast biology and heterogeneity, with a primary focus on the role of fibroblasts in skin wound repair. We also consider emerging techniques in the field, which enable an increasingly nuanced and contextualized understanding of these complex systems, and evaluate limitations of existing methodologies and knowledge. Collectively, this review spotlights a diverse body of research examining an often-overlooked cell type-the fibroblast-and its critical functions in wound repair and beyond.

The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review

BACKGROUND

Systemic sclerosis (SSc) is a rare connective tissue disease that can affect different organs and has extremely heterogenous presentations. This complexity makes it difficult to perform an early diagnosis and a subsequent subclassification of the disease. This hinders a personalized approach in clinical practice. In this context, machine learning (ML), a branch of artificial intelligence (AI), is able to recognize relationships in data and predict outcomes.

METHODS

Here, we performed a narrative review concerning the application of ML in SSc to define the state of art and evaluate its role in a precision medicine context.

RESULTS

Currently, ML has been used to stratify SSc patients and identify those at high risk of severe complications. Additionally, ML may be useful in the early detection of organ involvement. Furthermore, ML might have a role in target therapy approach and in predicting drug response.

CONCLUSION

Available evidence about the utility of ML in SSc is sparse but promising. Future improvements in this field could result in a big step toward precision medicine. Further research is needed to define ML application in clinical practice.

Effects of Immunoglobulins G From Systemic Sclerosis Patients in Normal Dermal Fibroblasts: A Multi-Omics Study

Autoantibodies (Aabs) are frequent in systemic sclerosis (SSc). Although recognized as potent biomarkers, their pathogenic role is debated. This study explored the effect of purified immunoglobulin G (IgG) from SSc patients on protein and mRNA expression of dermal fibroblasts (FBs) using an innovative multi-omics approach. Dermal FBs were cultured in the presence of sera or purified IgG from patients with diffuse cutaneous SSc (dcSSc), limited cutaneous SSc or healthy controls (HCs). The FB proteome and transcriptome were explored using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and microarray assays, respectively. Proteomic analysis identified 3,310 proteins. SSc sera and purified IgG induced singular protein profile patterns. These FB proteome changes depended on the Aab serotype, with a singular effect observed with purified IgG from anti-topoisomerase-I autoantibody (ATA) positive patients compared to HC or other SSc serotypes. IgG from ATA positive SSc patients induced enrichment in proteins involved in focal adhesion, cadherin binding, cytosolic part, or lytic vacuole. Multi-omics analysis was performed in two ways: first by restricting the analysis of the transcriptomic data to differentially expressed proteins; and secondly, by performing a global statistical analysis integrating proteomics and transcriptomics. Transcriptomic analysis distinguished 764 differentially expressed genes and revealed that IgG from dcSSc can induce extracellular matrix (ECM) remodeling changes in gene expression profiles in FB. Global statistical analysis integrating proteomics and transcriptomics confirmed that IgG from SSc can induce ECM remodeling and activate FB profiles. This effect depended on the serotype of the patient, suggesting that SSc Aab might play a pathogenic role in some SSc subsets.

Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification

Recently, bioinformatics and computational biology-enabled applications such as gene expression analysis, cellular restoration, medical image processing, protein structure examination, and medical data classification utilize fuzzy systems in offering effective solutions and decisions. The latest developments of fuzzy systems with artificial intelligence techniques enable to design the effective microarray gene expression classification models. In this aspect, this study introduces a novel feature subset selection with optimal adaptive neuro-fuzzy inference system (FSS-OANFIS) for gene expression classification. The major aim of the FSS-OANFIS model is to detect and classify the gene expression data. To accomplish this, the FSS-OANFIS model designs an improved grey wolf optimizer-based feature selection (IGWO-FS) model to derive an optimal subset of features. Besides, the OANFIS model is employed for gene classification and the parameter tuning of the ANFIS model is adjusted by the use of coyote optimization algorithm (COA). The application of IGWO-FS and COA techniques helps in accomplishing enhanced microarray gene expression classification outcomes. The experimental validation of the FSS-OANFIS model has been performed using Leukemia, Prostate, DLBCL Stanford, and Colon Cancer datasets. The proposed FSS-OANFIS model has resulted in a maximum classification accuracy of 89.47%.

[Social media-Chances and risks for rheumatology]

Die Nutzung von sozialen Medien (Social Media) und sozialen Netzwerken (Social Networks) hat in den letzten Jahren stark zugenommen. Sie gewinnen als Informationskanäle sowohl im privaten als auch beruflichen Kontext immer mehr an Bedeutung. Auch in der Medizin werden Social Media bereits vielfältig eingesetzt. So sind Fachgesellschaften und Interessenverbände immer stärker in den sozialen Netzwerken vertreten. Durch die breite Nutzung und große Reichweite der Netzwerke ergeben sich neue Möglichkeiten auch für das Fach der Rheumatologie. Dieser Übersichtsartikel gibt einen Überblick über die Charakteristika einiger großer Social-Media-Plattformen und untersucht bisherige Publikationen aus diesem Themengebiet im Rahmen einer systematischen Analyse. Weiterhin werden Vorteile, aber auch potenzielle Risiken, die bei der Nutzung entstehen können, beschrieben.

Skin involvement in early diffuse cutaneous systemic sclerosis: an unmet clinical need

Diffuse cutaneous systemic sclerosis (dcSSc) is associated with high mortality resulting from early internal-organ involvement. Clinicians therefore tend to focus on early diagnosis and treatment of potentially life-threatening cardiorespiratory and renal disease. However, the rapidly progressive painful, itchy skin tightening that characterizes dcSSc is the symptom that has the greatest effect on patients' quality of life, and there is currently no effective disease-modifying treatment for it. Considerable advances have been made in predicting the extent and rate of skin-disease progression (which vary between patients), including the development of techniques such as molecular analysis of skin biopsy samples. Risk stratification for progressive skin disease is especially relevant now that haematopoietic stem-cell transplantation is a treatment option, because stratification will inform the balance of risk versus benefit for each patient. Measurement of skin disease is a major challenge. Results from clinical trials have highlighted limitations of the modified Rodnan skin score (the current gold standard). Alternative patient-reported and other potential outcome measures have been and are being developed. Patients with early dcSSc should be referred to specialist centres to ensure best-practice management, including the management of their skin disease, and to maximize opportunities for inclusion in clinical trials.

Large-scale analysis of longitudinal skin gene expression in systemic sclerosis reveals relationships of immune cell and fibroblast activity with skin thickness and a trend towards normalisation over time

OBJECTIVES

Determine relationships between skin gene expression and systemic sclerosis (SSc) clinical disease features, and changes in skin gene expression over time.

METHODS

A total of 339 forearm skin biopsies were obtained from 113 SSc patients and 44 matched healthy controls. 105 SSc patients had a second biopsy, and 76 had a third biopsy. Global gene expression profiling was performed, and differentially expressed genes and cell type-specific signatures in SSc were evaluated for relationships to modified Rodnan Skin Score (mRSS) and other clinical variables. Changes in skin gene expression over time were analysed by mixed effects models and principal component analysis. Immunohistochemical staining was performed to validate conclusions.

RESULTS

Gene expression dysregulation was greater in SSc patients with affected skin than in those with unaffected skin. Immune cell and fibroblast signatures positively correlated with mRSS. High baseline immune cell and fibroblast signatures predicted higher mRSS over time, but were not independently predictive of longitudinal mRSS after adjustment for baseline mRSS. In early diffuse cutaneous SSc, immune cell and fibroblast signatures declined over time, and overall skin gene expression trended towards normalisation. On immunohistochemical staining, most early diffuse cutaneous SSc patients with high baseline T cell and macrophage numbers had declines in these numbers at follow-up.

CONCLUSIONS

Skin thickness in SSc is related to dysregulated immune cell and fibroblast gene expression. Skin gene expression changes over time in early diffuse SSc, with a tendency towards normalisation. These observations are relevant for understanding SSc pathogenesis and could inform treatment strategies and clinical trial design.

Life after Autologous Hematopoietic Stem Cell Transplantation for Systemic Sclerosis

Stem cell transplantation has been investigated as treatment for severe and progressive systemic sclerosis (SSc) for the past 25 years. To date, more than 1000 SSc patients have been transplanted worldwide. Overall and event-free survival have increased over the years, reflecting stricter patient selection criteria and better clinical management strategies. This review addresses long-term outcomes of transplanted SSc patients, considering phase I/II and randomized clinical trials, as well as observational studies and those assessing specific aspects of the disease. Clinical outcomes are discussed comparatively between studies, highlighting advances, drawbacks and controversies in the field. Areas for future development are also discussed.

Transcriptomic Evaluation of Juvenile Localized Scleroderma Skin With Histologic and Clinical Correlation

OBJECTIVE

Juvenile localized scleroderma (LS) is an autoimmune disease of the skin whose pathogenesis is not well understood due to the rarity of the disease. This study was undertaken to determine the skin transcriptome in skin biopsy tissue from children with juvenile LS compared to pediatric healthy controls, with identification of significant molecular targets using RNA sequencing (RNA-Seq). In this study, differentially expressed genes (DEGs) were assessed for correlations with histopathologic and clinical features in children with juvenile LS, and were used to group the children into distinct genetic clusters based on immunophenotype.

METHODS

RNA-Seq was performed on sections of paraffin-embedded skin tissue obtained from 28 children with juvenile LS and 10 pediatric healthy controls. RNA-Seq was carried out using an Illumina HTS TruSeq RNA Access library prep kit, with data aligned using STAR and data analysis using a DESeq2 platform. A standardized histologic scoring system was used to score skin sections for the severity of inflammation and levels of collagen deposition. Histologic scoring was completed by 2 pathologists who were blinded with regard to the status of each sample. Spearman's rank correlation coefficients were used to assess significant correlations between DEG expression profiles and skin histologic findings in patients with juvenile LS.

RESULTS

We identified 589 significant DEGs in children with juvenile LS as compared to healthy controls. Hierarchical clustering was used to demonstrate 3 distinct juvenile LS immunophenotype clusters. The histologic scores of skin inflammation (based on numbers and categories of inflammatory cell infiltrates) were significantly correlated with the expression levels of HLA-DPB1, HLA-DQA2, HLA-DRA, and STAT1 genes (r_s > 0.5, P < 0.01). Collagen thickness correlated with the expression levels of collagen organization genes as well as with genes found to be correlated with the severity of inflammation, including genes for major histocompatibility complex (MHC) class I, MHC class II, and interferon-γ signaling.

CONCLUSION

Among children with juvenile LS, 3 distinct genetic signatures, or clusters, were identified. In one cluster, inflammation-related pathways were up-regulated, corresponding to the histologic skin inflammation score. In the second cluster, fibrosis-related pathways were up-regulated. In the third cluster, gene expression in the skin corresponded to the patterns seen in healthy controls. Up-regulation of HLA class II genes was observed within the first cluster (characterized by predominant inflammation), a feature that has also been observed in the peripheral blood of patients with morphea and in the skin of patients with systemic sclerosis.

Integrative genomic expression analysis reveals stable differences between lung cancer and systemic sclerosis

BACKGROUND

The incidence and mortality of lung cancer are the highest among all cancers. Patients with systemic sclerosis show a four-fold greater risk of lung cancer than the general population. However, the underlying mechanism remains poorly understood.

METHODS

The expression profiles of 355 peripheral blood samples were integratedly analyzed, including 70 cases of lung cancer, 61 cases of systemic sclerosis, and 224 healthy controls. After data normalization and cleaning, differentially expressed genes (DEGs) between disease and control were obtained and deeply analyzed by bioinformatics methods. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed online by DAVID and KOBAS. The protein-protein interaction (PPI) networks were constructed from the STRING database.

RESULTS

From a total of 14,191 human genes, 299 and 1644 genes were identified as DEGs in systemic sclerosis and lung cancer, respectively. Among them, 64 DEGs were overlapping, including 36 co-upregulated, 10 co-downregulated, and 18 counter-regulated DEGs. Functional and enrichment analysis showed that the two diseases had common changes in immune-related genes. The expression of innate immune response and response to virus-related genes increased significantly, while the expression of negative regulation of cell cycle-related genes decreased notably. In contrast, the expression of mitophagy regulation, chromatin binding and fatty acid metabolism-related genes showed distinct trends.

CONCLUSIONS

Stable differences and similarities between systemic sclerosis and lung cancer were revealed. In peripheral blood, enhanced innate immunity and weakened negative regulation of cell cycle may be the common mechanisms of the two diseases, which may be associated with the high risk of lung cancer in systemic sclerosis patients. On the other hand, the counter-regulated DEGs can be used as novelbiomarkers of pulmonary diseases. In addition, fat metabolism-related DEGs were consideredto be associated with clinical blood lipid data.