Molecular stratification and precision medicine in systemic sclerosis from genomic and proteomic data

Profibrotic Role of Inducible Heat Shock Protein 90α Isoform in Systemic Sclerosis

Systemic sclerosis (SSc) is an autoimmune disease that affects skin and multiple internal organs. TGF-β, a central trigger of cutaneous fibrosis, activates fibroblasts with the involvement of the stress-inducible chaperone heat shock protein 90 isoform α (Hsp90α). Available evidence supports overexpression and secretion of Hsp90α as a feature in profibrotic pathological conditions. The aim of this work is to investigate the expression and function of Hsp90α in experimental models of skin fibrosis such as human fibroblasts, C57BL/6 mice, and in human SSc. For this purpose, we generated a new experimental model based on doxorubicin administration with improved characteristics with respect to the bleomycin model. We visualized disease progression in vivo by fluorescence imaging. In this work, we obtained Hsp90α mRNA overexpression in human skin fibroblasts, in bleomycin- and doxorubicin-induced mouse fibrotic skin, and in lungs of bleomycin- and doxorubicin-treated mice. Hsp90α-deficient mice showed significantly decreased skin thickness compared with wild-type mice in both animal models. In SSc patients, serum Hsp90α levels were increased in patients with lung involvement and in patients with the diffuse form of SSc (dSSc) compared with patients with the limited form of SSc. The serum Hsp90α levels of patients dSSc were correlated with the Rodnan score and the forced vital capacity variable. These results provide new supportive evidence of the contribution of the Hsp90α isoform in the development of skin fibrosis. In SSc, these results indicated that higher serum levels were associated with dSSc and lung fibrosis.

Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis

Background

Radiomic features calculated from routine medical images show great potential for personalised medicine in cancer. Patients with systemic sclerosis (SSc), a rare, multiorgan autoimmune disorder, have a similarly poor prognosis due to interstitial lung disease (ILD). Here, our objectives were to explore computed tomography (CT)-based high-dimensional image analysis (“radiomics”) for disease characterisation, risk stratification and relaying information on lung pathophysiology in SSc-ILD.

Methods

We investigated two independent, prospectively followed SSc-ILD cohorts (Zurich, derivation cohort, n=90; Oslo, validation cohort, n=66). For every subject, we defined 1355 robust radiomic features from standard-of-care CT images. We performed unsupervised clustering to identify and characterise imaging-based patient clusters. A clinically applicable prognostic quantitative radiomic risk score (qRISSc) for progression-free survival (PFS) was derived from radiomic profiles using supervised analysis. The biological basis of qRISSc was assessed in a cross-species approach by correlation with lung proteomic, histological and gene expression data derived from mice with bleomycin-induced lung fibrosis.

Results

Radiomic profiling identified two clinically and prognostically distinct SSc-ILD patient clusters. To evaluate the clinical applicability, we derived and externally validated a binary, quantitative radiomic risk score (qRISSc) composed of 26 features that accurately predicted PFS and significantly improved upon clinical risk stratification parameters in multivariable Cox regression analyses in the pooled cohorts. A high qRISSc score, which identifies patients at risk for progression, was reverse translatable from human to experimental ILD and correlated with fibrotic pathway activation.

Conclusions

Radiomics-based risk stratification using routine CT images provides complementary phenotypic, clinical and prognostic information significantly impacting clinical decision making in SSc-ILD.

CT-based radiomics decodes phenotypic, prognostic and molecular differences in SSc-ILD, and predicts progression-free survival with a significant impact on future clinical decision making in SSc-ILDhttps://bit.ly/3zPaMOn

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.

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.

Emerging Roles of Matricellular Proteins in Systemic Sclerosis

Systemic sclerosis is a rare chronic heterogenous disease that involves inflammation and vasculopathy, and converges in end-stage development of multisystem tissue fibrosis. The loss of tight spatial distribution and temporal expression of proteins in the extracellular matrix (ECM) leads to progressive organ stiffening, which is a hallmark of fibrotic disease. A group of nonstructural matrix proteins, known as matricellular proteins (MCPs) are implicated in dysregulated processes that drive fibrosis such as ECM remodeling and various cellular behaviors. Accordingly, MCPs have been described in the context of fibrosis in sclerosis (SSc) as predictive disease biomarkers and regulators of ECM synthesis, with promising therapeutic potential. In this present review, an informative summary of major MCPs is presented highlighting their clear correlations to SSc- fibrosis.

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.

Current and Future Outlook on Disease Modification and Defining Low Disease Activity in Systemic Sclerosis

Systemic sclerosis (SSc) is an autoimmune rheumatic disease with heterogeneous clinical manifestations and a variable course in which the severity of the pathology dictates the disease prognosis and course. Among autoimmune rheumatic diseases, SSc has the highest mortality rate among all rheumatic diseases, though there are exciting new therapeutic targets that appear to halt the progression of SSc manifestations such as skin or lung fibrosis. In selected patients, high-intensity regimens with autologous stem cell transplantation can favorably modify the course. In what was once thought to be an untreatable disease, targeted therapies have now changed the outlook of SSc to a treatable disorder. Herein, we discuss the targeted therapies modifying the outlook on selected organ involvement and creating opportunities for future treatment. We also present a framework for defining low disease activity in SSc.

The JAK/STAT pathway is activated in systemic sclerosis and is effectively targeted by tofacitinib

Rationale

Fibrosis leads to failure of the skin, lungs, and other organs in systemic sclerosis; accounts for substantial morbidity and mortality; and lacks effective therapy. Myofibroblast activation underlies organ fibrosis, but the key extracellular cues driving persistence of the process remain incompletely characterized.

Objectives

The objectives were to evaluate activation of the IL6/JAK/STAT axis associated with fibrosis in skin and lung biopsies from systemic sclerosis patients and effects of the Food and Drug Administration-approved JAK/STAT inhibitor, tofacitinib, on skin and lung fibrosis in animal models.

Methods

Bioinformatic analysis showed that IL6/JAK/STAT3 and tofacitinib gene signatures were aberrant in biopsies from systemic sclerosis patients in four independent cohorts. The results were confirmed by JAK and STAT3 phosphorylation in both skin and lung biopsies from patients with systemic sclerosis. Furthermore, treatment of mice with the selective JAK inhibitor tofacitinib not only prevented bleomycin-induced skin and lung fibrosis but also reduced skin fibrosis in TSK1/+ mice.

Conclusion

These findings implicate the JAK/STAT pathway in systemic sclerosis skin and lung fibrosis and identify tofacitinib as a potential antifibrotic agent for the treatment of systemic sclerosis and other fibrotic diseases.

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.

Current and future perspectives on management of systemic sclerosis-associated interstitial lung disease

Introduction: Systemic sclerosis (SSc) is a rare and complex connective tissue disease characterized by fibrosis of the skin and internal organs. Interstitial lung disease (ILD) is a common complication of SSc and the leading cause of SSc-related death. No drugs are licensed for the treatment of SSc-ILD. Areas covered: This review provides an overview of the current treatment of SSc-ILD and a perspective on investigational therapies, focusing on those studied in randomized controlled trials. Expert opinion: There is substantial room for improvement in the treatment of SSc-ILD. Current treatment focuses on immunosuppressant therapies, particularly cyclophosphamide and mycophenolate. Hematopoietic stem cell transplantation has been shown to improve long-term outcomes, but the risk of treatment-related mortality restricts its use to select patients at specialized centers. Modifying the course of disease to improve outcomes remains the goal for new therapies. Several drugs are under investigation as potential therapies for SSc-ILD, providing hope that the limited treatment armamentarium for SSc-ILD will be expanded and improved in the near future. Expert consensus is needed on how to screen for and monitor SSc-ILD and on when to initiate and escalate therapy.

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

OBJECTIVE

High-throughput gene expression profiling of tissue samples from patients with systemic sclerosis (SSc) has identified 4 "intrinsic" gene expression subsets: inflammatory, fibroproliferative, normal-like, and limited. Prior methods required agglomerative clustering of many samples. In order to classify individual patients in clinical trials or for diagnostic purposes, supervised methods that can assign single samples to molecular subsets are required. We undertook this study to introduce a novel machine learning classifier as a robust accurate intrinsic subset predictor.

METHODS

Three independent gene expression cohorts were curated and merged to create a data set covering 297 skin biopsy samples from 102 unique patients and controls, which was used to train a machine learning algorithm. We performed external validation using 3 independent SSc cohorts, including a gene expression data set generated by an independent laboratory on a different microarray platform. In total, 413 skin biopsy samples from 213 individuals were analyzed in the training and testing cohorts.

RESULTS

Repeated cross-fold validation identified consistent and discriminative markers using multinomial elastic net, performing with an average classification accuracy of 87.1% with high sensitivity and specificity. In external validation, the classifier achieved an average accuracy of 85.4%. Reanalyzing data from a previous study, we identified subsets of patients that represent the canonical inflammatory, fibroproliferative, and normal-like subsets.

CONCLUSION

We developed a highly accurate classifier for SSc molecular subsets for individual patient samples. The method can be used in SSc clinical trials to identify an intrinsic subset on individual samples. Our method provides a robust data-driven approach to aid clinical decision-making and interpretation of heterogeneous molecular information in SSc patients.

Evolving insights into the cellular and molecular pathogenesis of fibrosis in systemic sclerosis

Systemic sclerosis (SSc, scleroderma) is a complex multisystem disease characterized by autoimmunity, vasculopathy, and most notably, fibrosis. Multiple lines of evidence demonstrate a variety of emerging cellular and molecular pathways which are relevant to fibrosis in SSc. The myofibroblast remains the key effector cell in SSc. Understanding the development, differentiation, and function of the myofibroblast is therefore crucial to understanding the fibrotic phenotype of SSc. Studies now show that (1) multiple cell types give rise to myofibroblasts, (2) fibroblasts and myofibroblasts are heterogeneous, and (3) that a large number of (primarily immune) cells have important influences on the transition of fibroblasts to an activated myofibroblasts. In SSc, this differentiation process involves multiple pathways, including well known signaling cascades such as TGF-β and Wnt/β-Catenin signaling, as well as epigenetic reprogramming and a number of more recently defined cellular pathways. After reviewing the major and emerging cellular and molecular mechanisms underlying SSc, this article looks to identify clinical applications where this new molecular knowledge may allow for targeted treatment and personalized medicine approaches.

Baseline Plasma Cell Gene Signature Predicts Improvement in Systemic Sclerosis Skin Scores Following Treatment With Inebilizumab (MEDI-551) and Correlates With Disease Activity in Systemic Lupus Erythematosus and Chronic Obstructive Pulmonary Disease

OBJECTIVE

B cells impact the progression of systemic sclerosis (SSc; scleroderma) through multiple pathogenic mechanisms. CD19 inhibition in mice reduced skin thickness, collagen production, and autoantibody levels, consistent with CD19 expression on plasma cells (PCs), the source of antibody production. PC depletion could effectively reduce collagen deposition and inflammation in SSc; therefore, we investigated the effects of PC depletion on SSc disease activity.

METHODS

A PC gene signature was evaluated in SSc skin biopsy samples in 2 phase I clinical trials. We assessed microarray data from tissue from public studies of chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), dermatomyositis (DM), systemic lupus erythematosus (SLE), and atopic dermatitis, as well as blood from a phase IIb clinical trial in SLE.

RESULTS

The PC signature was elevated in SSc skin specimens compared to healthy donor skin (P = 2.28 × 10^-6 ) and correlated with the baseline modified Rodnan skin thickness score (MRSS) (r = 0.64, P = 0.0004). Patients with a high PC signature at baseline showed greater improvement in the MRSS (mean ± SD change 35 ± 16%; P = 6.30 × 10^-4 ) following anti-CD19 treatment with inebilizumab (MEDI-551) than did patients with a low PC signature at baseline (mean ± SD change 8 ± 12%; P = 0.104). The PC signature was overexpressed in tissue from patients with SLE, DM, COPD, interstitial lung disease, and IPF relative to controls (all fold change >2; P < 0.001). The PC signature also differed significantly between SLE patients with mild-to-moderate disease and those with severe disease (SLE Disease Activity Index cutoff at 10) (fold change 1.44; P = 3.90 × 10^-3 ) and correlated significantly with the degree of emphysema in COPD (r = 0.53, P = 7.55 × 10^-8 ).

CONCLUSION

Our results support the notion that PCs have a role in the pathogenesis of SSc and other autoimmune or pulmonary indications. An elevated pretreatment PC signature was associated with increased benefit from MEDI-551 in SSc.

Systemic sclerosis

Systemic sclerosis, also called scleroderma, is an immune-mediated rheumatic disease that is characterised by fibrosis of the skin and internal organs and vasculopathy. Although systemic sclerosis is uncommon, it has a high morbidity and mortality. Improved understanding of systemic sclerosis has allowed better management of the disease, including improved classification and more systematic assessment and follow-up. Additionally, treatments for specific complications have emerged and a growing evidence base supports the use of immune suppression for the treatment of skin and lung fibrosis. Some manifestations of the disease, such as scleroderma renal crisis, pulmonary arterial hypertension, digital ulceration, and gastro-oesophageal reflux, are now treatable. However, the burden of non-lethal complications associated with systemic sclerosis is substantial and is likely to become more of a challenge. Here, we review the clinical features of systemic sclerosis and describe the best practice approaches for its management. Furthermore, we identify future areas for development.

Current perspectives on the immunopathogenesis of systemic sclerosis

Systemic sclerosis (SSc or scleroderma) is a progressive and highly debilitating autoimmune disorder characterized by inflammation, vasculopathy, and extensive fibrosis. SSc is highly heterogeneous in its clinical presentation, extent and severity of skin and internal organ involvement, and clinical course and has the highest fatality rate among connective tissue diseases. While clinical outcomes have improved in recent years, no current therapy is able to reverse or slow the natural progression of SSc, a reflection of its complex pathogenesis. Although activation of the immune system has long been recognized, the mechanisms responsible for the initiation of autoimmunity and the role of immune effector pathways in the pathogenesis of SSc remain incompletely understood. This review summarizes recent progress in disease pathogenesis with particular focus on the immunopathogenetic mechanisms of SSc.