A Machine Learning Classifier for Assigning Individual Patients With Systemic Sclerosis to Intrinsic Molecular Subsets

The state of artificial intelligence for systemic dermatoses: Background and applications for psoriasis, systemic sclerosis, and much more

Artificial intelligence (AI) has been steadily integrated into dermatology, with AI platforms already attempting to identify skin cancers and diagnose benign versus malignant lesions. Although not as widely known, AI programs have also been utilized as diagnostic and prognostic tools for dermatologic conditions with systemic or extracutaneous involvement, especially for diseases with autoimmune etiologies. We have provided a primer on commonly used AI platforms and the practical applicability of these algorithms in dealing with psoriasis, systemic sclerosis, and dermatomyositis as a microcosm for future directions in the field. With a rapidly changing landscape in dermatology and medicine as a whole, AI could be a versatile tool to support clinicians and enhance access to care.

Application of artificial intelligence in rheumatic disease: a bibliometric analysis

The utilization of artificial intelligence (AI) in rheumatic diseases has enhanced the diagnostic accuracy of rheumatic diseases, enabled the prediction of patient outcomes, expanded treatment options, and facilitated the provision of individualized medical solutions. The research in this field has been progressively growing in recent years. Consequently, there is a need for bibliometric analysis to elucidate the current state of advancement and predominant research foci in AI applications within rheumatic diseases. Additionally, it is crucial to identify key contributors and their interrelations in this field. This study aimed to conduct a bibliometric analysis to investigate the current research hotspots and collaborative networks in the application of AI in rheumatic disease in recent years. A comprehensive search was conducted in Web of Science for articles on artificial intelligence in rheumatic diseases, published in SSCI and SCI-EXPANDED until January 1, 2024. Utilizing software tools like VOSviewers and CiteSpace, we analyzed various parameters including publication year, journal, country, institution, and authorship. This analysis extended to examining cited authors, generating reference and citation network graphs, and creating co-citation network and keyword maps. Additionally, research hotspots and trends in this domain were evaluated. As of January 1, 2024, a total of 3508 articles have been published on the application of artificial intelligence (AI) in rheumatic disease, exhibiting a steady rise in both the annual publication frequency and rate. "Scientific Reports" emerged as the leading journal in terms of relevant publications. The United States stood out as the predominant country in terms of the volume of published papers, with the University of California, San Francisco (UCSF) being the most prolific and frequently cited institution. Among authors, Young Ho Lee and Valentina Pedoia were noted for their significant contributions, with Pedoia achieving the highest average citation count per publication. Machine learning emerged as a prominent and central keyword. The trend indicates a growing interest in AI research within rheumatologic diseases, with its role expected to become increasingly pivotal in the field. This study presents a comprehensive summary of research trends and developments in the application of artificial intelligence (AI) in rheumatic diseases. It offers insights into potential collaborations and prospects for future research, clarifying the research frontiers and emerging directions in recent years. The findings of this study serve as a valuable reference for scholars studying rheumatology and immunology.

Fibroblast Subpopulations in Systemic Sclerosis: Functional Implications of Individual Subpopulations and Correlations with Clinical Features

Fibroblasts constitute a heterogeneous population of cells. In this study, we integrated single-cell RNA-sequencing and bulk RNA-sequencing data as well as clinical information to study the role of individual fibroblast populations in systemic sclerosis (SSc). SSc skin demonstrated an increased abundance of COMP+, COL11A1+, MYOC+, CCL19+, SFRP4/SFRP2+, and PRSS23/SFRP2+ fibroblasts signatures and decreased proportions of CXCL12+ and PI16+ fibroblast signatures in the Prospective Registry of Early Systemic Sclerosis and Genetics versus Environment in Scleroderma Outcome Study cohorts. Numerical differences were confirmed by multicolor immunofluorescence for selected fibroblast populations. COMP+, COL11A1+, SFRP4/SFRP2+, PRSS23/SFRP2+, and PI16+ fibroblasts were similarly altered between normal wound healing and patients with SSc. The proportions of profibrotic COMP+, COL11A1+, SFRP4/SFRP2+, and PRSS23/SFRP2+ and proinflammatory CCL19+ fibroblast signatures were positively correlated with clinical and histopathological parameters of skin fibrosis, whereas signatures of CXCL12+ and PI16+ fibroblasts were inversely correlated. Incorporating the proportions of COMP+, COL11A1+, SFRP4/SFRP2+, and PRSS23/SFRP2+ fibroblast signatures into machine learning models improved the classification of patients with SSc into those with progressive versus stable skin fibrosis. In summary, the profound imbalance of fibroblast subpopulations in SSc may drive the progression of skin fibrosis. Specific targeting of disease-relevant fibroblast populations may offer opportunities for the treatment of SSc and other fibrotic diseases.

Prognostic value of anti-IFI16 autoantibodies in pulmonary arterial hypertension and mortality in patients with systemic sclerosis

OBJECTIVES

To determine the diagnostic value of anti-interferon gamma inducible protein 16 (IFI16) autoantibodies in systemic sclerosis (SSc) patients negative for all tested SSc-specific autoantibodies (SSc-seronegative patients) and to evaluate the clinical significance of these autoantibodies, whether isolated or in the presence of anti-centromere autoantibodies (ACA).

METHODS

Overall, 58 SSc-seronegative and 66 ACA-positive patients were included in the study. All patients were tested for anti-IFI16 autoantibodies by an in-house direct ELISA. Associations between clinical parameters and anti-IFI16 autoantibodies were analysed.

RESULTS

Overall, 17.2% of SSc-seronegative and 39.4% of ACA-positive patients were positive for anti-IFI16 autoantibodies. Anti-IFI16 autoantibodies were found only in patients within the limited cutaneous SSc (lcSSc) subset. A positive association between anti-IFI16 positivity and isolated pulmonary arterial hypertension (PAH) was found (odds ratio [OR]=5.07; p=0.014) even after adjusting for ACA status (OR=4.99; p=0.019). Anti-IFI16-positive patients were found to have poorer overall survival than negative patients (p=0.032). Cumulative survival rates at 10, 20 and 30 years were 96.9%, 92.5% and 68.7% for anti-IFI16-positive patients vs. 98.8%, 97.0% and 90.3% for anti-IFI16-negative-patients, respectively. Anti-IFI16-positive patients also had worse overall survival than anti-IFI16-negative patients after adjusting for ACA status in the multivariate Cox analysis (hazard ratio [HR]=3.21; p=0.043).

CONCLUSION

Anti-IFI16 autoantibodies were associated with isolated PAH and poorer overall survival. Anti-IFI16 autoantibodies could be used as a supplementary marker of lcSSc in SSc-seronegative patients and for identifying ACA-positive patients with worse clinical outcome.

Enrichment strategies for clinical trials targeting skin fibrosis and interstitial lung disease in systemic sclerosis

PURPOSE OF REVIEW

This review gives an update on enrichment strategies for clinical trials in patients with systemic sclerosis (SSc) in two contexts - skin fibrosis in early diffuse cutaneous disease, and SSc-related interstitial lung disease (ILD) - focusing on reports from the last 18 months. Lessons have been learnt from recent studies, making this review timely.

RECENT FINDINGS

Recent trials have highlighted how patients included into trials must be carefully selected to include 'progressors', that is, those most likely to benefit from treatment, and how drug mechanism action of action will influence trial design. For skin fibrosis, current enrichment strategies are mainly on clinical grounds (including disease duration, extent of skin thickening, tendon friction rubs and anti-RNA polymerase III positivity). Gene expression signatures may play a role in the future. For ILD, current enrichment strategies (degree of lung involvement as assessed by pulmonary function and high-resolution computed tomography) may help to recruit the most informative patients, but should avoid being too stringent to be feasible or for findings to be generalizable.

SUMMARY

Both skin fibrosis and ILD trials are challenging in SSc. Ongoing work on enrichment strategies should help to differentiate effective new treatments from placebo with smaller sample sizes than have been included in recent studies.

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.

Clinical Phenotypes of Patients With Systemic Sclerosis With Distinct Molecular Signatures in Skin

OBJECTIVE

Systemic sclerosis (SSc) patients are classified according to degree of skin fibrosis (limited and diffuse cutaneous [lc and dc]) and serum autoantibodies. We undertook the present multicenter study to determine whether intrinsic subset (IS) classification based upon skin gene expression yields additional valuable clinical information.

METHODS

SSc patients and healthy participants (HPs) were classified into Normal-like, Limited, Fibroproliferative, and Inflammatory ISs using a previously trained classifier. Clinical data were obtained (serum autoantibodies, pulmonary function testing, modified Rodnan skin thickness scores [mRSS], and high-resolution chest computed tomography [HRCT]). Statistical analyses were performed to compare patients classified by IS, traditional cutaneous classification, and serum autoantibodies.

RESULTS

A total of 223 participants (165 SSc [115 dcSSc and 50 lcSSc] and 58 HPs) were classified. Inflammatory IS patients had higher mRSS (22.1 ± 9.9; P < 0.001) than other ISs and dcSSc patients (19.4 ± 9.4; P = 0.05) despite similar disease duration (median [interquartile range] months 14.9 [19.9] vs. 18.4 [31.6]; P = 0.48). In multivariable modeling, no significant association between mRSS and RNA polymerase III (P = 0.07) or anti-topoisomerase I (Scl-70) (P = 0.09) was found. Radiographic interstitial lung disease (ILD) was more prevalent in Fibroproliferative IS compared with other ISs (91%; P = 0.04) with similar prevalence between lcSSc and dcSSc (67% vs. 76%; P = 0.73). Positive Scl-70 antibody was the strongest ILD predictor (P < 0.001). Interestingly, all lcSSc/Fibroproliferative patients demonstrated radiographic ILD.

CONCLUSIONS

Classification by IS identifies patients with distinct clinical phenotypes versus traditional cutaneous or autoantibody classification. IS classification identifies subgroups of SSc patients with more radiographic ILD (Fibroproliferative), higher mRSS (Inflammatory), and milder phenotype (Normal-like) and may provide additional clinically useful information to current SSc classification systems.

Therapeutic Approaches to Systemic Sclerosis: Recent Approvals and Future Candidate Therapies

Systemic sclerosis is the rheumatic disease with the highest individual mortality. The severity of the disease is determined by the extent of fibrotic changes to cutaneous and internal organ tissues, the most life-threatening visceral manifestations being interstitial lung disease, SSc-associated-pulmonary arterial hypertension and myocardial involvement. The heterogeneity of the disease has initially hindered the design of successful clinical trials, but considerations on classification criteria have improved patient selection in trials, allowing the identification of more homogeneous groups of patients based on progressive visceral manifestations or the extent of skin involvement with a focus of patients with early disease. Two major subsets of systemic sclerosis are classically described: limited cutaneous systemic sclerosis characterized by distal skin fibrosis and the diffuse subset with distal and proximal skin thickening. Beyond this dichotomic subgrouping of systemic sclerosis, new phenotypic considerations based on antibody subtypes have provided a better understanding of the heterogeneity of the disease, anti-Scl70 antibodies being associated with progressive interstitial lung disease regardless of cutaneous involvement. Two targeted therapies, tocilizumab (a monoclonal antibody targeting interleukin-6 receptors (IL-6R)) and nintedanib (a tyrosine kinase inhibitor), have recently been approved by the American Food & Drug Administration to limit the decline of lung function in patients with SSc-associated interstitial lung disease, demonstrating that such better understanding of the disease pathogenesis with the identification of key targets can lead to therapeutic advances in the management of some visceral manifestations of the disease. This review will provide a brief overview of the pathogenesis of SSc and will present a selection of therapies recently approved or evaluated in this context. Therapies evaluated and approved in SSc-ILD will be emphasized and a review of recent phase II trials in diffuse cutaneous systemic sclerosis will be proposed. We will also discuss selected therapeutic pathways currently under investigation in systemic sclerosis that still lack clinical data in this context but that may show promising results in the future based on preclinical data.

Scleroderma Skin: How Is Treatment Best Guided by Data and Implemented in Clinical Practice?

As skin involvement is the hall mark of systemic sclerosis (SSc) and changes of skin involvement have shown to correlate with internal organ involvement, assessing the extend of skin involvement is key. Although the modified Rodnan skin score is a validated tool used to evaluate the skin in SSc, it has its drawbacks. Novel imagine methods are promising but should be further evaluated. As for molecule markers for skin progression there are conflicting data on the predictive significance of baseline SSc skin gene expression profiles, but immune cell type signature in SSc skin correlates with progression.

Differentially expressed genes in systemic sclerosis: Towards predictive medicine with new molecular tools for clinicians

Systemic sclerosis (SSc) is a rare and chronic autoimmune disease characterized by a pathogenic triad of immune dysregulation, vasculopathy, and progressive fibrosis. Clinical tools commonly used to assess patients, such as the modified Rodnan skin score, difference between limited or diffuse forms of skin involvement, presence of lung, heart or kidney involvement, or of various autoantibodies, are important prognostic factors, but still fail to reflect the large heterogeneity of the disease. SSc treatment options are diverse, ranging from conventional drugs to autologous hematopoietic stem cell transplantation, and predicting response is challenging. Genome-wide technologies, such as high throughput microarray analyses and RNA sequencing, allow accurate, unbiased, and broad assessment of alterations in expression levels of multiple genes. In recent years, many studies have shown robust changes in the gene expression profiles of SSc patients compared to healthy controls, mainly in skin tissues and peripheral blood cells. The objective analysis of molecular patterns in SSc is a powerful tool that can further classify SSc patients with similar clinical phenotypes and help predict response to therapy. In this review, we describe the journey from the first discovery of differentially expressed genes to the identification of enriched pathways and intrinsic subsets identified in SSc, using machine learning algorithms. Finally, we discuss the use of these new tools to predict the efficacy of various treatments, including stem cell transplantation. We suggest that the use of RNA gene expression-based classifications according to molecular subsets may bring us one step closer to precision medicine in Systemic Sclerosis.

Clinical and Molecular Findings After Autologous Stem Cell Transplantation or Cyclophosphamide for Scleroderma: Handling Missing Longitudinal Data

OBJECTIVE

Among individuals with systemic sclerosis (SSc) randomized to cyclophosphamide (CYC) (n = 34) or hematopoietic stem cell transplantation (HSCT) (n = 33), we examined longitudinal trends of clinical, pulmonary function, and quality of life measures while accounting for the influence of early failures on treatment comparisons.

METHODS

Assuming that data were missing at random, mixed-effects regression models were used to estimate longitudinal trends for clinical measures when comparing treatment groups. Results were compared to observed means and to longitudinal trends estimated from shared parameter models, assuming that data were missing not at random. Longitudinal trends for SSc intrinsic molecular subsets defined by baseline gene expression signatures (normal-like, inflammatory, and fibroproliferative signatures) were also studied.

RESULTS

Available observed means for pulmonary function tests appeared to improve over time in both arms. However, after accounting for participant loss, forced vital capacity in HSCT recipients increased by 0.77 percentage points/year but worsened by -3.70/year for CYC (P = 0.004). Similar results were found for diffusing capacity for carbon monoxide and quality of life indicators. Results for both analytic models were consistent. HSCT recipients in the inflammatory (n = 20) and fibroproliferative (n = 20) subsets had superior long-term trends compared to CYC for pulmonary and quality of life measures. HSCT was also superior for modified Rodnan skin thickness scores in the fibroproliferative subset. For the normal-like subset (n = 22), superiority of HSCT was less apparent.

CONCLUSION

Longitudinal trends estimated from 2 statistical models affirm the efficacy of HSCT over CYC in severe SSc. Failure to account for early loss of participants may distort estimated clinical trends over the long term.

Bioinformatics-integrated screening of systemic sclerosis-specific expressed markers to identify therapeutic targets

Background

Systemic sclerosis (SSc) is a rare autoimmune disease characterized by extensive skin fibrosis. There are no effective treatments due to the severity, multiorgan presentation, and variable outcomes of the disease. Here, integrated bioinformatics was employed to discover tissue-specific expressed hub genes associated with SSc, determine potential competing endogenous RNAs (ceRNA) regulatory networks, and identify potential targeted drugs.

Methods

In this study, four datasets of SSc were acquired. To identify the genes specific to tissues or organs, the BioGPS web database was used. For differentially expressed genes (DEGs), functional and enrichment analyses were carried out, and hub genes were screened and shown in a network of protein-protein interactions (PPI). The potential lncRNA-miRNA-mRNA ceRNA network was constructed using the online databases. The specifically expressed hub genes and ceRNA network were validated in the SSc mouse and in normal mice. We also used the receiver operating characteristic (ROC) curve to determine the diagnostic values of effective biomarkers in SSc. Finally, the Drug-Gene Interaction Database (DGIdb) identified specific medicines linked to hub genes.

Results

The pooled datasets identified a total of 254 DEGs. The tissue/organ-specifically expressed genes involved in this analysis are commonly found in the hematologic/immune system and bone/muscle tissue. The enrichment analysis of DEGs revealed the significant terms such as regulation of actin cytoskeleton, immune-related processes, the VEGF signaling pathway, and metabolism. Cytoscape identified six gene cluster modules and 23 hub genes. And 4 hub genes were identified, including Serpine1, CCL2, IL6, and ISG15. Consistently, the expression of Serpine1, CCL2, IL6, and ISG15 was significantly higher in the SSc mouse model than in normal mice. Eventually, we found that MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1 may be promising RNA regulatory pathways in SSc. Besides, ten potential therapeutic drugs associated with the hub gene were identified.

Conclusions

This study revealed tissue-specific expressed genes, SERPINE1, CCL2, IL6, and ISG15, as effective biomarkers and provided new insight into the mechanisms of SSc. Potential RNA regulatory pathways, including MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1, contribute to our knowledge of SSc. Furthermore, the analysis of drug-hub gene interactions predicted TIPLASININ, CARLUMAB and BINDARIT as candidate drugs for SSc.

A genomic meta-analysis of clinical variables and their association with intrinsic molecular subsets in systemic sclerosis

OBJECTIVES

Four intrinsic molecular subsets (inflammatory, fibroproliferative, limited, normal-like) have previously been identified in SSc and are characterized by unique gene expression signatures and pathways. The intrinsic subsets have been linked to improvement with specific therapies. Here, we investigated associations between baseline demographics and intrinsic molecular subsets in a meta-analysis of published datasets.

METHODS

Publicly available gene expression data from skin biopsies of 311 SSc patients measured by DNA microarray were classified into the intrinsic molecular subsets. RNA-sequencing data from 84 participants from the ASSET trial were used as a validation cohort. Baseline clinical demographics and intrinsic molecular subsets were tested for statistically significant associations.

RESULTS

Males were more likely to be classified in the fibroproliferative subset (P = 0.0046). SSc patients who identified as African American/Black were 2.5 times more likely to be classified as fibroproliferative compared with White/Caucasian patients (P = 0.0378). ASSET participants sera positive for anti-RNA pol I and RNA pol III autoantibodies were enriched in the inflammatory subset (P = 5.8 × 10-5, P = 9.3 × 10-5, respectively), while anti-Scl-70 was enriched in the fibroproliferative subset. Mean modified Rodnan Skin Score (mRSS) was statistically higher in the inflammatory and fibroproliferative subsets compared with normal-like (P = 0.0027). The average disease duration for inflammatory subset was less than fibroproliferative and normal-like intrinsic subsets (P = 8.8 × 10-4).

CONCLUSIONS

We identified multiple statistically significant differences in baseline demographics between the intrinsic subsets that may represent underlying features of disease pathogenesis (e.g. chronological stages of fibrosis) and have implications for treatments that are more likely to work in certain SSc populations.

Machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via CD28 pathway modulation

Here, the efficacy of abatacept in patients with early diffuse systemic sclerosis (dcSSc) was analyzed to test the hypothesis that patients in the inflammatory intrinsic subset would show the most significant clinical improvement. Eighty-four participants with dcSSc were randomized to receive abatacept or placebo for 12 months. RNA-Seq was performed on 233 skin paired biopsies at baseline and at 3 and 6 months. Improvement was defined as a 5-point or more than 20% change in modified Rodnan skin score (mRSS) between baseline and 12 months. Samples were assigned to intrinsic gene expression subsets (inflammatory, fibroproliferative, or normal-like subsets). In the abatacept arm, change in mRSS was most pronounced for the inflammatory and normal-like subsets relative to the placebo subset. Gene expression for participants on placebo remained in the original molecular subset, whereas inflammatory participants treated with abatacept had gene expression that moved toward the normal-like subset. The Costimulation of the CD28 Family Reactome Pathway decreased in patients who improved on abatacept and was specific to the inflammatory subset. Patients in the inflammatory subset had elevation of the Costimulation of the CD28 Family pathway at baseline relative to that of participants in the fibroproliferative and normal-like subsets. There was a correlation between improved ΔmRSS and baseline expression of the Costimulation of the CD28 Family pathway. This study provides an example of precision medicine in systemic sclerosis clinical trials.

Precision medicine: the precision gap in rheumatic disease

For many oncological conditions, the application of timely and patient-tailored targeted therapies, or precision medicine, is a major therapeutic development that has provided considerable clinical benefit. However, despite the application of increasingly sophisticated technologies, alongside advanced bioinformatic and machine-learning algorithms, this success is yet to be replicated for the rheumatic diseases. In rheumatoid arthritis, for example, despite an array of targeted biologic and conventional therapeutics, treatment choice remains largely based on trial and error. The concept of the 'precision gap' for rheumatic disease can help us to identify factors that underpin the slow progress towards the discovery and adoption of precision-medicine approaches for rheumatic disease. In a rheumatic disease such as rheumatoid arthritis, it is possible to identify four themes that have slowed progress, solutions to which should help to close the precision gap. These themes relate to our fundamental understanding of disease pathogenesis, how we determine treatment response, confounders of treatment outcomes and trial design.

Systemic sclerosis in adults. Part I: Clinical features and pathogenesis

Systemic sclerosis (SSc), also referred to as systemic scleroderma or scleroderma, is a rare, complex immune-mediated connective tissue disease characterized by progressive skin fibrosis and other clinically heterogenous features. The etiopathogenesis of SSc involves vasculopathy and immune system dysregulation occurring on a permissive genetic and epigenetic background, ultimately leading to fibrosis. Recent developments in our understanding of disease-specific autoantibodies and bioinformatic analyses has led to a reconsideration of the purely clinical classification of diffuse and limited cutaneous SSc subgroups. Autoantibody profiles are predictive of skin and internal organ involvement and disease course. Early diagnosis of SSc, with commencement of disease-modifying treatment, has the potential to improve patient outcomes. In SSc, many of the clinical manifestations that present early signs of disease progression and activity are cutaneous, meaning dermatologists can and should play a key role in the diagnosis and management of this significant condition. The first article in this continuing medical education series discusses the epidemiology, clinical characteristics, and pathogenesis of SSc in adults, with an emphasis on skin manifestations, the important role of dermatologists in recognizing these, and their correlation with systemic features and disease course.

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.

The promise of precision medicine in rheumatology

Systemic autoimmune rheumatic diseases (SARDs) exhibit extensive heterogeneity in clinical presentation, disease course, and treatment response. Therefore, precision medicine - whereby treatment is tailored according to the underlying pathogenic mechanisms of an individual patient at a specific time - represents the 'holy grail' in SARD clinical care. Current strategies include treat-to-target therapies and autoantibody testing for patient stratification; however, these are far from optimal. Recent innovations in high-throughput 'omic' technologies are now enabling comprehensive profiling at multiple levels, helping to identify subgroups of patients who may taper off potentially toxic medications or better respond to current molecular targeted therapies. Such advances may help to optimize outcomes and identify new pathways for treatment, but there are many challenges along the path towards clinical translation. In this Review, we discuss recent efforts to dissect cellular and molecular heterogeneity across multiple SARDs and future directions for implementing stratification approaches for SARD treatment in the clinic.

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.

Cellular and Molecular Diversity in Scleroderma

With the increasing armamentarium of high-throughput tools available at manageable cost, it is attractive and informative to determine the molecular underpinnings of patient heterogeneity in systemic sclerosis (SSc). Given the highly variable clinical outcomes of patients labelled with the same diagnosis, unravelling the cellular and molecular basis of disease heterogeneity will be crucial to predicting disease risk, stratifying management and ultimately informing a patient-centered precision medicine approach. Herein, we summarise the findings of the past several years in the fields of genomics, transcriptomics, and proteomics that contribute to unraveling the cellular and molecular heterogeneity of SSc. Expansion of these findings and their routine integration with quantitative analysis of histopathology and imaging studies into clinical care promise to inform a scientifically driven patient-centred personalized medicine approach to SSc in the near future.

Systematic Analysis of the Literature in Search of Defining Systemic Sclerosis Subsets

OBJECTIVE

Systemic sclerosis (SSc) is a multisystem disease with heterogeneity in presentation and prognosis.An international collaboration to develop new SSc subset criteria is underway. Our objectives were to identify systems of SSc subset classification and synthesize novel concepts to inform development of new criteria.

METHODS

Medline, Cochrane MEDLINE, the Cumulative Index to Nursing and Allied Health Literature, EMBASE, and Web of Science were searched from their inceptions to December 2019 for studies related to SSc subclassification, limited to humans and without language or sample size restrictions.

RESULTS

Of 5686 citations, 102 studies reported original data on SSc subsets. Subset classification systems relied on extent of skin involvement and/or SSc-specific autoantibodies (n = 61), nailfold capillary patterns (n = 29), and molecular, genomic, and cellular patterns (n = 12). While some systems of subset classification confer prognostic value for clinical phenotype, severity, and mortality, only subsetting by gene expression signatures in tissue samples has been associated with response to therapy.

CONCLUSION

Subsetting on extent of skin involvement remains important. Novel disease attributes including SSc-specific autoantibodies, nailfold capillary patterns, and tissue gene expression signatures have been proposed as innovative means of SSc subsetting.

A Machine Learning Application to Predict Early Lung Involvement in Scleroderma: A Feasibility Evaluation

Introduction: Systemic sclerosis (SSc) is a systemic immune-mediated disease, featuring fibrosis of the skin and organs, and has the greatest mortality among rheumatic diseases. The nervous system involvement has recently been demonstrated, although actual lung involvement is considered the leading cause of death in SSc and, therefore, should be diagnosed early. Pulmonary function tests are not sensitive enough to be used for screening purposes, thus they should be flanked by other clinical examinations; however, this would lead to a risk of overtesting, with considerable costs for the health system and an unnecessary burden for the patients. To this extent, Machine Learning (ML) algorithms could represent a useful add-on to the current clinical practice for diagnostic purposes and could help retrieve the most useful exams to be carried out for diagnostic purposes. Method: Here, we retrospectively collected high resolution computed tomography, pulmonary function tests, esophageal pH impedance tests, esophageal manometry and reflux disease questionnaires of 38 patients with SSc, applying, with R, different supervised ML algorithms, including lasso, ridge, elastic net, classification and regression trees (CART) and random forest to estimate the most important predictors for pulmonary involvement from such data. Results: In terms of performance, the random forest algorithm outperformed the other classifiers, with an estimated root-mean-square error (RMSE) of 0.810. However, this algorithm was seen to be computationally intensive, leaving room for the usefulness of other classifiers when a shorter response time is needed. Conclusions: Despite the notably small sample size, that could have prevented obtaining fully reliable data, the powerful tools available for ML can be useful for predicting early lung involvement in SSc patients. The use of predictors coming from spirometry and pH impedentiometry together might perform optimally for predicting early lung involvement in SSc.

Machine Learning in Rheumatic Diseases

With advances in information technology, the demand for using data science to enhance healthcare and disease management is rapidly increasing. Among these technologies, machine learning (ML) has become ubiquitous and indispensable for solving complex problems in many scientific fields, including medical science. ML allows the development of guidelines and framing of the evaluation system for complex diseases based on massive data. In the analysis of rheumatic diseases, which are chronic and remarkably heterogeneous, ML can be anticipated to be extremely helpful in deciphering and revealing the inherent interrelationships in disease development and progression, which can further enhance the overall understanding of the disease, optimize patients' stratification, calibrate therapeutic strategies, and predict prognosis and outcomes. In this review, the basics of ML, its potential clinical applications in rheumatology, together with its strengths and limitations are summarized.

Current Concepts on the Pathogenesis of Systemic Sclerosis

From the clinical standpoint, systemic sclerosis (SSc) is characterized by skin and internal organ fibrosis, diffuse fibroproliferative vascular modifications, and autoimmunity. Clinical presentation and course are highly heterogenous and life expectancy variably affected mostly dependent on lung and heart involvement. SSc touches more women than men with differences in disease severity and environmental exposure. Pathogenetic events originate from altered homeostasis favored by genetic predisposition, environmental cues and a variety of endogenous and exogenous triggers. Epigenetic modifications modulate SSc pathogenesis which strikingly associate profound immune-inflammatory dysregulation, abnormal endothelial cell behavior, and cell trans-differentiation into myofibroblasts. SSc myofibroblasts show enhanced survival and enhanced extracellular matrix deposition presenting altered structure and altered physicochemical properties. Additional cell types of likely pathogenic importance are pericytes, platelets, and keratinocytes in conjunction with their relationship with vessel wall cells and fibroblasts. In SSc, the profibrotic milieu is favored by cell signaling initiated in the one hand by transforming growth factor-beta and related cytokines and in the other hand by innate and adaptive type 2 immune responses. Radical oxygen species and invariant receptors sensing danger participate to altered cell behavior. Conventional and SSc-specific T cell subsets modulate both fibroblasts as well as endothelial cell dysfunction. Beside autoantibodies directed against ubiquitous antigens important for enhanced clinical classification, antigen-specific agonistic autoantibodies may have a pathogenic role. Recent studies based on single-cell RNAseq and multi-omics approaches are revealing unforeseen heterogeneity in SSc cell differentiation and functional states. Advances in system biology applied to the wealth of data generated by unbiased screening are allowing to subgroup patients based on distinct pathogenic mechanisms. Deciphering heterogeneity in pathogenic mechanisms will pave the way to highly needed personalized therapeutic approaches.

Molecular basis for clinical diversity between autoantibody subsets in diffuse cutaneous systemic sclerosis

OBJECTIVES

Clinical heterogeneity is a cardinal feature of systemic sclerosis (SSc). Hallmark SSc autoantibodies are central to diagnosis and associate with distinct patterns of skin-based and organ-based complications. Understanding molecular differences between patients will benefit clinical practice and research and give insight into pathogenesis of the disease. We aimed to improve understanding of the molecular differences between key diffuse cutaneous SSc subgroups as defined by their SSc-specific autoantibodies METHODS: We have used high-dimensional transcriptional and proteomic analysis of blood and the skin in a well-characterised cohort of SSc (n=52) and healthy controls (n=16) to understand the molecular basis of clinical diversity in SSc and explore differences between the hallmark antinuclear autoantibody (ANA) reactivities.

RESULTS

Our data define a molecular spectrum of SSc based on skin gene expression and serum protein analysis, reflecting recognised clinical subgroups. Moreover, we show that antitopoisomerase-1 antibodies and anti-RNA polymerase III antibodies specificities associate with remarkably different longitudinal change in serum protein markers of fibrosis and divergent gene expression profiles. Overlapping and distinct disease processes are defined using individual patient pathway analysis.

CONCLUSIONS

Our findings provide insight into clinical diversity and imply pathogenetic differences between ANA-based subgroups. This supports stratification of SSc cases by ANA antibody subtype in clinical trials and may explain different outcomes across ANA subgroups in trials targeting specific pathogenic mechanisms.

An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis

Hand Radiography (RA) is one of the prime tests for checking the progress of rheumatoid joint inflammation in human bone joints. Recognizing the specific phase of RA is a difficult assignment, as human abilities regularly curb the techniques for it. Convolutional neural network (CNN) is the center for hand recognition for recognizing complex examples. The human cerebrum capacities work in a high-level way, so CNN has been planned depending on organic neural-related organizations in humans for imitating its unpredictable capacities. This article accordingly presents the convolutional neural network (CNN) which has the ability to naturally gain proficiency with the qualities and anticipate the class of hand radiographs from an expansive informational collection. The reproduction of the CNN halfway layers, which depict the elements of the organization, is likewise appeared. For arrangement of the model, a dataset of 290 radiography images is utilized. The result indicates that hand X-rays are rated with an accuracy of 94.46% by the proposed methodology. Our experiments show that the network sensitivity is observed to be 0.95 and the specificity is observed to be 0.82.

A computational workflow for the detection of candidate diagnostic biomarkers of Kawasaki disease using time-series gene expression data

Unlike autoimmune diseases, there is no known constitutive and disease-defining biomarker for systemic autoinflammatory diseases (SAIDs). Kawasaki disease (KD) is one of the "undiagnosed" types of SAIDs whose pathogenic mechanism and gene mutation still remain unknown. To address this issue, we have developed a sequential computational workflow which clusters KD patients with similar gene expression profiles across the three different KD phases (Acute, Subacute and Convalescent) and utilizes the resulting clustermap to detect prominent genes that can be used as diagnostic biomarkers for KD. Self-Organizing Maps (SOMs) were employed to cluster patients with similar gene expressions across the three phases through inter-phase and intra-phase clustering. Then, false discovery rate (FDR)-based feature selection was applied to detect genes that significantly deviate across the per-phase clusters. Our results revealed five genes as candidate biomarkers for KD diagnosis, namely, the HLA-DQB1, HLA-DRA, ZBTB48, TNFRSF13C, and CASD1. To our knowledge, these five genes are reported for the first time in the literature. The impact of the discovered genes for KD diagnosis against the known ones was demonstrated by training boosting ensembles (AdaBoost and XGBoost) for KD classification on common platform and cross-platform datasets. The classifiers which were trained on the proposed genes from the common platform data yielded an average increase by 4.40% in accuracy, 5.52% in sensitivity, and 3.57% in specificity than the known genes in the Acute and Subacute phases, followed by a notable increase by 2.30% in accuracy, 2.20% in sensitivity, and 4.70% in specificity in the cross-platform analysis.

Novel classifications for systemic sclerosis: challenging historical subsets to unlock new doors

PURPOSE OF REVIEW

Systemic sclerosis (SSc) is a severe rheumatic disease characterized by a considerable heterogeneity in clinical presentations and pathophysiological mechanisms. This variability has a substantial impact on morbidity and mortality and limits the generalizability of clinical trial results. This review aims to highlight recent studies that have proposed new innovative approaches to decipher this heterogeneity, in particular, by attempting to optimize disease classification.

RECENT FINDINGS

The historical dichotomy limited/diffuse subsets based on cutaneous involvement has been challenged by studies highlighting an underestimated heterogeneity between these two subtypes and showing that presence of organ damage and autoantibody profiles markedly influenced survival beyond skin extension. Advanced computational methods using unsupervised machine learning analyses of clinical variables and/or high-throughput omics technologies, clinical variables trajectories modelling overtime or radiomics have provided significant insights on key pathogenic processes that could help defining new subgroups beyond the diffuse/limited subsets.

SUMMARY

We can anticipate that a future classification of SSc patients will integrate innovative approaches encompassing clinical phenotypes, variables trajectories, serological features and innovative omics molecular signatures. It nevertheless seems crucial to also pursue the implementation and standardization of readily available and easy to use tools that can be used in clinical practice.

Plasticity of Treg and imbalance of Treg/Th17 cells in patients with systemic sclerosis modified by FK506

To determine the effects of Tacrolimus (FK506) on Treg cells and subpopulations in SSc patients and assess the ability of FK506 to modify the immune imbalance of Treg/Th17 cells. We analyzed PBMC from five SSc patients and six healthy control by flow cytometry after cultured with 0, 0.1, 1, or 10 ng/ml FK506 in vitro. The number of Treg cells decreased in SSc patients treated with FK506. The number of FrI cells were decreased in SSc following FK506 treatment. The drug did increase the frequency of FrII/Treg cells, but not FrII cells. However, FK506 significantly decreased FrIII in both SSc patients and controls. FK506 clearly decreased the numbers of Th17 cells and FoxP3⁺IL-17⁺ cells. The proliferation capacity of cells was also inhibited by FK506, which had a greater effect on FoxP3^- cells than FoxP3⁺ cells. FK506 did inhibit the proliferation of FrIII cells, but not FrI or FrII cells. Our study provides that FK506 reduced the number of FoxP3^low CD45RA^- T cells (FrIII) by inhibiting its proliferation. Therefore, FK506 modifies Treg cells and the immune imbalance between Tregs and Th17 cells in SSc patients.

A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening

Aim

In osteoarthritis (OA) there is a need for automated screening systems for early detection of structural progressors. We built a comprehensive machine learning (ML) model that bridges major OA risk factors and serum levels of adipokines/related inflammatory factors at baseline for early prediction of at-risk knee OA patient structural progressors over time.

Methods

The patient- and gender-based model development used baseline serum levels of six adipokines, three related inflammatory factors and their ratios (36), as well as major OA risk factors [age and bone mass index (BMI)]. Subjects (677) were selected from the Osteoarthritis Initiative (OAI) progression subcohort. The probability values of being structural progressors (PVBSP) were generated using our previously published prediction model, including five baseline structural features of the knee, i.e. two X-rays and three magnetic resonance imaging variables. To identify the most important variables amongst the 47 studied in relation to PVBSP, we employed the ML feature classification methodology. Among five supervised ML algorithms, the support vector machine (SVM) demonstrated the best accuracy and use for gender-based classifiers development. Performance and sensitivity of the models were assessed. A reproducibility analysis was performed with clinical trial OA patients.

Results

Feature selections revealed that the combination of age, BMI, and the ratios CRP/MCP-1 and leptin/CRP are the most important variables in predicting OA structural progressors in both genders. Classification accuracies for both genders in the testing stage (OAI) were >80%, with the highest sensitivity of CRP/MCP-1. Reproducibility analysis showed an accuracy ⩾92%; the ratio CRP/MCP-1 demonstrated the highest sensitivity in women and leptin/CRP in men.

Conclusion

This is the first time that such a framework was built for predicting knee OA structural progressors. Using this automated ML patient- and gender-based model, early prediction of knee structural OA progression can be performed with high accuracy using only three baseline serum biomarkers and two risk factors.

Plain language summary

Machine learning model for early knee osteoarthritis structural progression Knee osteoarthritis is a well-known debilitating disease leading to reduced mobility and quality of life - the main causes of chronic invalidity. Disease evolution can be slow and span many years; however, for some individuals, the progression/evolution can be fast. Current treatments are only symptomatic and conventional diagnosis of osteoarthritis is not very effective in early identification of patients who will progress rapidly. To improve therapeutic approaches, we need a robust prediction model to stratify osteoarthritis patients at an early stage according to risk of joint structure disease progression.We hypothesize that a prediction model using a machine learning system would enable such an early identification of individuals for whom osteoarthritis knee structure will degrade rapidly. Data were from the Osteoarthritis Initiative, a National Institute of Health (United States) databank, and the robustness and generalizability of the developed model was further evaluated using osteoarthritis patients from an external cohort. Using the supervised machine learning system (support vector machine), we developed an automated patient- and gender-based model enabling an early clinical prognosis for individuals at high risk of structural progressive osteoarthritis. In brief, this model employed at baseline (when the subject sees a physician) easily obtained features consisting of the two main osteoarthritis risk factors, age and bone mass index (BMI), in addition to the serum levels of three molecules. Two of these molecules belong to a family of factors names adipokines and one to a related inflammatory factor. In brief, the model comprising a combination of age, BMI, and the ratios CRP/MCP-1 and leptin/CRP were found very robust for both genders, and the high accuracy persists when tested with an external cohort conferring the gender-based model generalizability. This study offers a new automated system for identifying early knee osteoarthritis structural progressors, which will significantly improve clinical prognosis with real time patient monitoring.

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

OBJECTIVE

We sought to determine histologic and gene expression features of clinical improvement in early diffuse cutaneous systemic sclerosis (dcSSc; scleroderma).

METHODS

Fifty-eight forearm biopsies were evaluated from 26 individuals with dcSSc in two clinical trials. Histologic/immunophenotypic assessments of global severity, alpha-smooth muscle actin (aSMA), CD34, collagen, inflammatory infiltrate, follicles and thickness were compared with gene expression and clinical data. Support vector machine learning was performed using scleroderma gene expression subset (normal-like, fibroproliferative, inflammatory) as classifiers and histology scores as inputs. Comparison of w-vector mean absolute weights was used to identify histologic features most predictive of gene expression subset. We then tested for differential gene expression according to histologic severity and compared those with clinical improvement (according to the Combined Response Index in Systemic Sclerosis).

RESULTS

aSMA was highest and CD34 lowest in samples with highest local Modified Rodnan Skin Score. CD34 and aSMA changed significantly from baseline to 52 weeks in clinical improvers. CD34 and aSMA were the strongest predictors of gene expression subset, with highest CD34 staining in the normal-like subset (p<0.001) and highest aSMA staining in the inflammatory subset (p=0.016). Analysis of gene expression according to CD34 and aSMA binarised scores identified a 47-gene fibroblast polarisation signature that decreases over time only in improvers (vs non-improvers). Pathway analysis of these genes identified gene expression signatures of inflammatory fibroblasts.

CONCLUSION

CD34 and aSMA stains describe distinct fibroblast polarisation states, are associated with gene expression subsets and clinical assessments, and may be useful biomarkers of clinical severity and improvement in dcSSc.

Targeting CD38-dependent NAD+ metabolism to mitigate multiple organ fibrosis

The processes underlying synchronous multiple organ fibrosis in systemic sclerosis (SSc) remain poorly understood. Age-related pathologies are associated with organismal decline in nicotinamide adenine dinucleotide (NAD⁺) that is due to dysregulation of NAD⁺ homeostasis and involves the NADase CD38. We now show that CD38 is upregulated in patients with diffuse cutaneous SSc, and CD38 levels in the skin associate with molecular fibrosis signatures, as well as clinical fibrosis scores, while expression of key NAD⁺-synthesizing enzymes is unaltered. Boosting NAD⁺ via genetic or pharmacological CD38 targeting or NAD⁺ precursor supplementation protected mice from skin, lung, and peritoneal fibrosis. In mechanistic experiments, CD38 was found to reduce NAD⁺ levels and sirtuin activity to augment cellular fibrotic responses, while inhibiting CD38 had the opposite effect. Thus, we identify CD38 upregulation and resulting disrupted NAD⁺ homeostasis as a fundamental mechanism driving fibrosis in SSc, suggesting that CD38 might represent a novel therapeutic target.

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CD38 shows elevated expression in skin biopsies of patients with systemic sclerosis

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Elevated CD38 is associated with reduced NAD⁺ and augmented fibrotic responses

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Genetic loss of CD38 is associated with increased NAD⁺ levels and attenuated fibrosis

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NAD⁺ boosting via CD38 inhibition or NR supplementation prevents multi-organ fibrosis

Emerging drugs for the treatment of scleroderma: a review of recent phase 2 and 3 trials

INTRODUCTION

Systemic sclerosis (SSc) has the highest case-specific mortality of all connective tissue diseases. Its underlying disease mechanism affects several organs and remains incompletely understood. Ongoing work clarifying its etiopathogenesis is helping to develop targeted therapy.

AREAS COVERED

Several clinical trials have evaluated the safety and efficacy of agents targeting different mechanisms of this disease. This review article reviews those mechanisms and surveys four key recent phase II or III clinical trials that are contributing to the landscape of SSc therapy. The reported trials primarily focus on patients with systemic sclerosis in the early phase of disease.

EXPERT OPINION

Traditional therapies for SSc center on immunosuppressive and cytotoxic agents. A new cadre of therapies is borne from improved understandings of SSc pathobiology and target the inflammatory-fibrotic pathways. Scleroderma trials have entered the initial phase of personalized medicine, recognizing molecular subsets that will improve upon cohort enrichment and maximize the measurable benefit of future therapies.

Molecular "omic" signatures in systemic sclerosis

Systemic sclerosis (SSc) is a connective tissue disorder characterized by immunologic, vascular, and extracellular matrix abnormalities. Variation in the proportion and/or timing of activation in the deregulated molecular pathways that underlie SSc may explain the observed clinical heterogeneity in terms of disease phenotype and treatment response. In recent years, SSc research has generated massive amounts of "omics" level data. In this review, we discuss the body of "omics" level work in SSc and how each layer provides unique insight to our understanding of SSc. We posit that effective integration of genomic, transcriptomic, metagenomic, and epigenomic data is an important step toward precision medicine and is vital to the identification of effective therapeutic options for patients with SSc.

High-frequency Ultrasound Assessment of Systemic Sclerosis Skin Involvement: Intraobserver Repeatability and Relationship With Clinician Assessment and Dermal Collagen Content

OBJECTIVE

The modified Rodnan skin score (mRSS) remains the preferred method for skin assessment in systemic sclerosis (SSc). There are concerns regarding high interobserver variability of mRSS and negative clinical trials utilizing mRSS as the primary endpoint. High-frequency ultrasound (HFUS) allows objective assessment of cutaneous fibrosis in SSc. We investigated the relationship between HFUS with both mRSS and dermal collagen.

METHODS

Skin thickness (ST), echogenicity, and novel shear wave elastography (SWE) were assessed in 53 patients with SSc and 15 healthy controls (HCs) at the finger, hand, forearm, and abdomen. The relationship between HFUS parameters with mRSS (n = 53) and dermal collagen (10 patients with SSc and 10 HCs) was investigated. Intraobserver repeatability of HFUS was calculated using intraclass correlation coefficients (ICCs).

RESULTS

HFUS assessment of ST (hand/forearm) and SWE (finger/hand) correlated with local mRSS at some sites. Subclinical abnormalities in ST, echogenicity, and SWE were present in clinically uninvolved SSc skin. Additionally, changes in echogenicity and SWE were sometimes apparent despite objectively normal ST on HFUS. ST, SWE, and local mRSS correlated strongly with collagen quantification (r = 0.697, 0.709, 0.649, respectively). Intraobserver repeatability was high for all HFUS parameters (ICCs for ST = 0.946-0.978; echogenicity = 0.648-0.865; and SWE = 0.953-0.973).

CONCLUSION

Our data demonstrate excellent reproducibility and reassuring convergent validity with dermal collagen content. Detection of subclinical abnormalities is an additional benefit of HFUS. The observed correlations with collagen quantification support further investigation of HFUS as an alternative to mRSS in clinical trial settings.

Machine learning predicts stem cell transplant response in severe scleroderma

OBJECTIVE

The Scleroderma: Cyclophosphamide or Transplantation (SCOT) trial demonstrated clinical benefit of haematopoietic stem cell transplant (HSCT) compared with cyclophosphamide (CYC). We mapped PBC (peripheral blood cell) samples from the SCOT clinical trial to scleroderma intrinsic subsets and tested the hypothesis that they predict long-term response to HSCT.

METHODS

We analysed gene expression from PBCs of SCOT participants to identify differential treatment response. PBC gene expression data were generated from 63 SCOT participants at baseline and follow-up timepoints. Participants who completed treatment protocol were stratified by intrinsic gene expression subsets at baseline, evaluated for event-free survival (EFS) and analysed for differentially expressed genes (DEGs).

RESULTS

Participants from the fibroproliferative subset on HSCT experienced significant improvement in EFS compared with fibroproliferative participants on CYC (p=0.0091). In contrast, EFS did not significantly differ between CYC and HSCT arms for the participants from the normal-like subset (p=0.77) or the inflammatory subset (p=0.1). At each timepoint, we observed considerably more DEGs in HSCT arm compared with CYC arm with HSCT arm showing significant changes in immune response pathways.

CONCLUSIONS

Participants from the fibroproliferative subset showed the most significant long-term benefit from HSCT compared with CYC. This study suggests that intrinsic subset stratification of patients may be used to identify patients with SSc who receive significant benefit from HSCT.

Regulator combinations identify systemic sclerosis patients with more severe disease

Systemic sclerosis (SSc) is a heterogeneous autoimmune disorder that results in skin fibrosis, autoantibody production, and internal organ dysfunction. We previously identified 4 “intrinsic” subsets of SSc based upon skin gene expression that are found across organ systems. Gene expression regulators that underlie the SSc-intrinsic subsets, or are associated with clinical covariates, have not been systematically characterized. Here, we present a computational framework to calculate the activity scores of gene expression regulators and identify their associations with SSc clinical outcomes. We found that regulator activity scores can reproduce the intrinsic molecular subsets, with distinct sets of regulators identified for inflammatory, fibroproliferative, limited, and normal-like samples. Regulators most highly correlated with modified Rodnan skin score (MRSS) also varied by intrinsic subset. We identified subgroups of patients with fibroproliferative and inflammatory SSc with more severe pathophenotypes, such as higher MRSS and increased likelihood of interstitial lung disease (ILD). Using an independent cohort, we show that the group with more severe ILD was more likely to show forced vital capacity decline over a period of 36–54 months. Our results demonstrate an association among the activation of regulators, gene expression subsets, and clinical variables that can identify patients with SSc with more severe disease.

An association between the activation of regulators, gene expression subsets, and clinical variables identifies systemic sclerosis patients with more severe disease.

Big data in systemic sclerosis: Great potential for the future

Since it was first used in 1997, the term “big data” has been popularized; however, the concept of big data is relatively new to medicine. Big data refers to a method and technique to systematically retrieve, collect, manage, and analyze very large and complex sets of structured and unstructured data that cannot be sufficiently processed using traditional methods of processing data. Integrating big data in rare diseases with low prevalence and incidence, like systemic sclerosis is of particular importance. We conducted a literature review of use of big data in systemic sclerosis. The volume of data on systemic sclerosis has grown steadily in the recent years; however, big data methods have not been readily used. This inexhaustible source of data needs to be used more to unleash its full potential.

Biomarkers in systemic sclerosis

PURPOSE OF REVIEW

To discuss recent advances in identification of biomarkers in systemic sclerosis for disease severity, prognosis, and treatment response.

RECENT FINDINGS

Recent reports describe novel circulating markers of disease severity, autoantibody associations with specific manifestations including cancer, and skin gene expression-based predictors of modified Rodnan skin score progression and treatment response. Moreover, there is converging evidence that C-reactive protein and pneumoproteins such as Krebs von den Lungen-6 and chemokine ligand 18 could serve as prognostic biomarkers in systemic sclerosis-associated interstitial lung disease.

SUMMARY

Several novel biomarkers show promise in improving the assessment of systemic sclerosis (SSc) disease severity, prognosis, and treatment response. Their potential utility in prospective selection of patients for clinical trials and in individual patient management require additional research.

Autoantibodies to stratify systemic sclerosis patients into clinically actionable subsets

Systemic sclerosis (SSc) is a rare chronic disease of unknown etiology characterized by vascular abnormalities and fibrosis involving the skin and internal organs, especially the gastrointestinal tract, lung, heart and kidneys. Although the disease was historically stratified according to the extent of skin involvement, more recent approaches place more emphasis on patterns and extent of internal organ involvement. Despite numerous clinical trials, disease-modifying treatment options are still limited resulting in persistent poor quality of life and high mortality. This review provides an overview of autoantibodies in SSc and novel approaches to stratify the disease into clinically actionable subsets.

Abatacept in Early Diffuse Cutaneous Systemic Sclerosis: Results of a Phase II Investigator-Initiated, Multicenter, Double-Blind, Randomized, Placebo-Controlled Trial

OBJECTIVE

T cells play a key role in the pathogenesis of early systemic sclerosis. This study was undertaken to assess the safety and efficacy of abatacept in patients with diffuse cutaneous systemic sclerosis (dcSSc).

METHODS

In this 12-month, randomized, double-blind, placebo-controlled trial, participants were randomized 1:1 to receive either subcutaneous abatacept 125 mg or matching placebo, stratified by duration of dcSSc. Escape therapy was allowed at 6 months for worsening disease. The coprimary end points were change in the modified Rodnan skin thickness score (MRSS) compared to baseline and safety over 12 months. Differences in longitudinal outcomes were assessed according to treatment using linear mixed models, with outcomes censored after initiation of escape therapy. Skin tissue obtained from participants at baseline was classified into intrinsic gene expression subsets.

RESULTS

Among 88 participants, the adjusted mean change in the MRSS at 12 months was -6.24 units for those receiving abatacept and -4.49 units for those receiving placebo, with an adjusted mean treatment difference of -1.75 units (P = 0.28). Outcomes for 2 secondary measures (Health Assessment Questionnaire disability index and a composite measure) were clinically and statistically significantly better with abatacept. The proportion of subjects in whom escape therapy was needed was higher in the placebo group relative to the abatacept group (36% versus 16%). In the inflammatory and normal-like skin gene expression subsets, decline in the MRSS over 12 months was clinically and significantly greater in the abatacept group versus the placebo group (P < 0.001 and P = 0.03, respectively). In the abatacept group, adverse events occurred in 35 participants versus 40 participants in the placebo group, including 2 deaths and 1 death, respectively.

CONCLUSION

In this phase II trial, abatacept was well-tolerated, but change in the MRSS was not statistically significant. Secondary outcome measures, including gene expression subsets, showed evidence in support of abatacept. These data should be confirmed in a phase III trial.

Hematopoietic Stem Cell Transplantation for Systemic Sclerosis: Review of Current Status

Three prospective controlled clinical trials and numerous small series and case reports have confirmed that durable, drug-free remission in systemic sclerosis is possible via an autologous hematopoietic stem cell transplantation. Similar results have been seen in other autoimmune diseases. The exact mechanism by which this immune "reset" was achieved in some but not all cases remains elusive, but includes major reduction of autoreactive immune competent cells, re-establishment of T- and B cell regulatory networks and normalization of tissue niche function, particularly vascular. Some aspects regarding mobilization, conditioning and graft manipulation still remain open, but clearly a significant toxicity is associated with all effective regimens at present, and therefore patient selection remains a key issue. In the hematology/oncology arena, major efforts are being made to reduce genotoxic and other collateral toxicity induced by current mobilization and conditioning protocols, which may also translate to autoimmune disease. These include developments in rapid mobilization and antibody drug conjugate conditioning technology. If effective, such low-toxicity regimens might be applied to autoimmune disease at an earlier stage before chronicity of autoimmunity has been established, thus changing the therapeutic paradigm.

Global skin gene expression analysis of early diffuse cutaneous systemic sclerosis shows a prominent innate and adaptive inflammatory profile

OBJECTIVES

Determine global skin transcriptome patterns of early diffuse systemic sclerosis (SSc) and how they differ from later disease.

METHODS

Skin biopsy RNA from 48 patients in the Prospective Registry for Early Systemic Sclerosis (PRESS) cohort (mean disease duration 1.3 years) and 33 matched healthy controls was examined by next-generation RNA sequencing. Data were analysed for cell type-specific signatures and compared with similarly obtained data from 55 previously biopsied patients in Genetics versus Environment in Scleroderma Outcomes Study cohort with longer disease duration (mean 7.4 years) and their matched controls. Correlations with histological features and clinical course were also evaluated.

RESULTS

SSc patients in PRESS had a high prevalence of M2 (96%) and M1 (94%) macrophage and CD8 T cell (65%), CD4 T cell (60%) and B cell (69%) signatures. Immunohistochemical staining of immune cell markers correlated with the gene expression-based immune cell signatures. The prevalence of immune cell signatures in early diffuse SSc patients was higher than in patients with longer disease duration. In the multivariable model, adaptive immune cell signatures were significantly associated with shorter disease duration, while fibroblast and macrophage cell type signatures were associated with higher modified Rodnan Skin Score (mRSS). Immune cell signatures also correlated with skin thickness progression rate prior to biopsy, but did not predict subsequent mRSS progression.

CONCLUSIONS

Skin in early diffuse SSc has prominent innate and adaptive immune cell signatures. As a prominently affected end organ, these signatures reflect the preceding rate of disease progression. These findings could have implications in understanding SSc pathogenesis and clinical trial design.