Consensus gene modules strategy identifies candidate blood-based biomarkers for primary Sjögren's disease.

Primary Sjögren disease (pSD) is an autoimmune disease characterized by lymphoid infiltration of exocrine glands leading to dryness of the mucosal surfaces and by the production of autoantibodies. The pathophysiology of pSD remains elusive and no treatment with demonstrated efficacy is available yet. To better understand the biology underlying pSD heterogeneity, we aimed at identifying Consensus gene Modules (CMs) that summarize the high-dimensional transcriptomic data of whole blood samples in pSD patients. We performed unsupervised gene classification on four data sets and identified thirteen CMs. We annotated and interpreted each of these CMs as corresponding to cell type abundances or biological functions by using gene set enrichment analyses and transcriptomic profiles of sorted blood cell subsets. Correlation with independently measured cell type abundances by flow cytometry confirmed these annotations. We used these CMs to reconcile previously proposed patient stratifications of pSD. Importantly, we showed that the expression of modules representing lymphocytes and erythrocytes before treatment initiation is associated with response to hydroxychloroquine and leflunomide combination therapy in a clinical trial. These consensus modules will help the identification and translation of blood-based predictive biomarkers for the treatment of pSD.

Model-based computational precision medicine to develop combination therapies for autoimmune diseases.

INTRODUCTION: The complex pathophysiology of autoimmune diseases (AIDs) is being progressively deciphered, providing evidence for a multiplicity of pro-inflammatory pathways underlying heterogeneous clinical phenotypes and disease evolution. AREAS COVERED: Treatment strategies involving drug combinations are emerging as a preferred option to achieve remission in a vast majority of patients affected by systemic AIDs. The design of appropriate drug combinations can benefit from AID modeling following a comprehensive multi-omics molecular profiling of patients combined with Artificial Intelligence (AI)-powered computational analyses. Such disease models support patient stratification in homogeneous subgroups, shed light on dysregulated pro-inflammatory pathways and yield hypotheses regarding potential therapeutic targets and candidate biomarkers to stratify and monitor patients during treatment. AID models inform the rational design of combination therapies interfering with independent pro-inflammatory pathways related to either one of five prominent immune compartments contributing to the pathophysiology of AIDs, i.e. pro-inflammatory signals originating from tissues, innate immune mechanisms, T lymphocyte activation, autoantibodies and B cell activation, as well as soluble mediators involved in immune cross-talk. EXPERT OPINION: The optimal management of AIDs in the future will rely upon rationally designed combination therapies, as a modality of a model-based Computational Precision Medicine taking into account the patients' biological and clinical specificities.

Industrializing AI-powered drug discovery: lessons learned from the Patrimony computing platform.

INTRODUCTION: As a mid-size international pharmaceutical company, we initiated 4 years ago the launch of a dedicated high-throughput computing platform supporting drug discovery. The platform named 'Patrimony' was built up on the initial predicate to capitalize on our proprietary data while leveraging public data sources in order to foster a Computational Precision Medicine approach with the power of artificial intelligence. AREAS COVERED: Specifically, Patrimony is designed to identify novel therapeutic target candidates. With several successful use cases in immuno-inflammatory diseases, and current ongoing extension to applications to oncology and neurology, we document how this industrial computational platform has had a transformational impact on our R&D, making it more competitive, as well time and cost effective through a model-based educated selection of therapeutic targets and drug candidates. EXPERT OPINION: We report our achievements, but also our challenges in implementing data access and governance processes, building up hardware and user interfaces, and acculturing scientists to use predictive models to inform decisions.

IFN-α: A key therapeutic target for multiple autoimmune rheumatic diseases.

Interferon (IFN)-α has emerged as a major therapeutic target for several autoimmune rheumatic diseases. In this review, we focus on clinical and preclinical advances in anti-IFN-α treatments in systemic lupus erythematosus (SLE), primary Sjögren syndrome (pSS), systemic sclerosis (SSc), and dermatomyositis (DM), for which a high medical need persists. Promising achievements were obtained following direct IFN-α neutralization, targeting its production through the cytosolic nucleic acid sensor pathways or by blocking its downstream effects through the type I IFN receptor. We further focus on molecular profiling and data integration approaches as crucial steps to select patients most likely to benefit from anti-IFN-α therapies within a precision medicine approach.

Network-based repurposing identifies anti-alarmins as drug candidates to control severe lung inflammation in COVID-19.

While establishing worldwide collective immunity with anti SARS-CoV-2 vaccines, COVID-19 remains a major health issue with dramatic ensuing economic consequences. In the transition, repurposing existing drugs remains the fastest cost-effective approach to alleviate the burden on health services, most particularly by reducing the incidence of the acute respiratory distress syndrome associated with severe COVID-19. We undertook a computational repurposing approach to identify candidate therapeutic drugs to control progression towards severe airways inflammation during COVID-19. Molecular profiling data were obtained from public sources regarding SARS-CoV-2 infected epithelial or endothelial cells, immune dysregulations associated with severe COVID-19 and lung inflammation induced by other respiratory viruses. From these data, we generated a protein-protein interactome modeling the evolution of lung inflammation during COVID-19 from inception to an established cytokine release syndrome. This predictive model assembling severe COVID-19-related proteins supports a role for known contributors to the cytokine storm such as IL1ß, IL6, TNFα, JAK2, but also less prominent actors such as IL17, IL23 and C5a. Importantly our analysis points out to alarmins such as TSLP, IL33, members of the S100 family and their receptors (ST2, RAGE) as targets of major therapeutic interest. By evaluating the network-based distances between severe COVID-19-related proteins and known drug targets, network computing identified drugs which could be repurposed to prevent or slow down progression towards severe airways inflammation. This analysis confirmed the interest of dexamethasone, JAK2 inhibitors, estrogens and further identified various drugs either available or in development interacting with the aforementioned targets. We most particularly recommend considering various inhibitors of alarmins or their receptors, currently receiving little attention in this indication, as candidate treatments for severe COVID-19.

The IgG2 Isotype of Anti-Transcription Intermediary Factor 1γ Autoantibodies Is a Biomarker of Cancer and Mortality in Adult Dermatomyositis.

OBJECTIVE: Anti-transcription intermediary factor 1γ (anti-TIF1γ) antibodies are the main predictors of cancer in dermatomyositis (DM). Yet, a substantial proportion of anti-TIF1γ-positive DM patients do not develop cancer. This study was undertaken to identify biomarkers to better evaluate the risk of cancer and mortality in DM. METHODS: This multicenter study was conducted in adult anti-TIF1γ-positive DM patients from August 2013 to August 2017. Anti-TIF1γ autoantibody levels and IgG subclasses were identified using a newly developed quantitative immunoassay. Age, sex, DM signs and activity, malignancy, and creatine kinase (CK) level were recorded. Risk factors were determined by univariate and multivariate analysis according to a Cox proportional hazards regression model. RESULTS: Among the 51 adult patients enrolled (mean ± SD age 61 ± 17 years; ratio of men to women 0.65), 40 (78%) had cancer and 21 (41%) died, with a mean ± SD survival time of 10 ± 6 months. Detection of anti-TIF1γ IgG2 was significantly associated with mortality (P = 0.0011) and occurrence of cancer during follow-up (P < 0.0001), with a 100% positive predictive value for cancer when the mean fluorescence intensity of anti-TIF1γ IgG2 was >385. None of the patients developed cancer after 24 months of follow-up. Univariate survival analyses showed that mortality was also associated with age >60 years (P = 0.0003), active DM (P = 0.0042), cancer (P = 0.0031), male sex (P = 0.011), and CK level >1,084 units/liter (P = 0.005). Multivariate analysis revealed that age >60 years (P = 0.015) and the presence of anti-TIF1γ IgG2 (P = 0.048) were independently associated with mortality. CONCLUSION: Our findings indicate that anti-TIF1γ IgG2 is a potential new biomarker of cancer that should be helpful in identifying the risk of mortality in anti-TIF1γ-positive DM patients.

Dermatomyositis and Immune-Mediated Necrotizing Myopathies: A Window on Autoimmunity and Cancer.

Autoimmune myopathies (myositides) are strongly associated with malignancy. The link between myositis and cancer, originally noticed by Bohan and Peter in their classification in 1975 (1), has been evidenced by large population-based cohort studies and a recent meta-analysis. The numerous reports of cases in which the clinical course of myositis reflects that of cancer and the short delay between myositis and cancer onset support the notion that myositis may be an authentic paraneoplastic disorder. Thus, cancer-associated myositis raises the question of cancer as a cause rather than a consequence of autoimmunity. Among myositides, dermatomyositis and more recently, although to a lesser extent, immune-mediated necrotizing myopathies are the most documented forms associated with cancer. Interestingly, the current diagnostic approach for myositis is based on the identification of specific antibodies where each antibody determines specific clinical features and outcomes. Recent findings have shown that the autoantibodies anti-TIF1γ, anti-NXP2 and anti-HMGCR are associated with cancers in the course of myositis. Herein, we highlight the fact that the targets of these three autoantibodies involve cellular pathways that intervene in tumor promotion and we discuss the role of cancer mutations as autoimmunity triggers in adult myositis.