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.

High-content multimodal analysis supports the IL-7/IL-7 receptor axis as a relevant therapeutic target in primary Sjögren's syndrome.

OBJECTIVE: While the involvement of IL-7/IL-7R axis in pSS has been described in relation to T cells, little is known about the contribution of this pathway in relationship with other immune cells, and its implication in autoimmunity. Using high-content multiomics data, we aimed at characterizing IL-7R expressing cells and the involvement of IL-7/IL-7R pathway in pSS pathophysiology. METHODS: An IL-7 signature established using RNA-sequencing of human PBMCs incubated with IL-7 was applied to 304 pSS patients, and on RNA-Seq datasets from tissue biopsies. High-content immunophenotyping using flow and imaging mass cytometry was developed to characterize peripheral and in situ IL-7R expression. RESULTS: We identified a blood 4-gene IL-7 module (IKZF4, KIAA0040, PGAP1 and SOS1) associated with anti-SSA/Ro positiveness in patients as well as disease activity, and a tissue 5-gene IL-7 module (IL7R, PCED1B, TNFSF8, ADAM19, MYBL1) associated with infiltration severity. We confirmed expression of IL-7R on T cells subsets, and further observed upregulation of IL-7R on double-negative (DN) B cells, and especially DN2 B cells. IL-7R expression was increased in pSS compared to sicca patients with variations seen according to the degree of infiltration. When expressed, IL-7R was mainly found on epithelial cells, CD4+ and CD8+ T cells, switched memory B cells, DN B cells and M1 macrophages. CONCLUSION: This exhaustive characterization of the IL-7/IL-7R pathway in pSS pathophysiology established that two IL-7 gene modules discriminate pSS patients with a high IL-7 axis involvement. Their use could guide the implementation of an anti-IL-7R targeted therapy in a precision medicine approach.

Biomarker-driven development of new therapies for autoimmune diseases: current status and future promises.

INTRODUCTION: Auto-immune diseases are complex and heterogeneous. Various types of biomarkers can be used to support precision medicine approaches to autoimmune diseases, ensuring that the right patient receives the most appropriate therapy to improve treatment outcomes. AREAS COVERED: We review the recent progress made in modeling several autoimmune diseases such as Systemic Lupus Erythematosus, primary Sjogren Syndrome, and Rheumatoid Arthritis following extensive molecular profiling of large cohorts of patients. From this knowledge, BMKs are being identified which support diagnostic as well as patient stratification and prediction of response to treatment. The identification of biomarkers should be initiated early in drug development and properly validated during subsequent clinical trials. To ensure the robustness and reproducibility of biomarkers, the PERMIT Consortium recently established recommendations highlighting the importance of relevant study design, sample size, and appropriate validation of analytical methods. EXPERT OPINION: The integration by AI-powered analytics of massive data provided by multi-omics technologies, high-resolution medical imaging and sensors borne by patients will eventually allow the identification of clinically relevant BMKs, likely in the form of combinatorial predictive algorithms, to support future drug development for autoimmune diseases.

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.

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.

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.

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

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

Applications of transcriptomics in support of drug development for osteoarthritis.

Objective: Understanding the heterogeneity and pathophysiology of osteoarthritis (OA) is critical to support the development of tailored disease-modifying treatments. To this aim, transcriptomics tools are highly relevant to delineate dysregulated molecular pathways and identify new therapeutic targets. Methods: We review the methodology and outcomes of transcriptomics studies conducted in OA, based on a comprehensive literature search of the PubMed and Google Scholar databases using the terms "osteoarthritis", "OA", "knee OA", "hip OA", "genes", "RNA-seq", "microarray", "transcriptomic" and "PCR" as key words. Beyond target-focused RT-qPCR, more comprehensive techniques include microarrays, RNA sequencing (RNA-seq) and single cell RNA-seq analyses. Results: The standardization of those methods to ensure the quality of both RNA extraction and sequencing is critical to get meaningful insights. Transcriptomics studies have been conducted in various tissues involved in the pathogenesis of OA, including articular cartilage, subchondral bone and synovium, as well as in the blood of patients. Molecular pathways dysregulated in OA relate to cartilage degradation, matrix and bone remodeling, neurogenic pain, inflammation, apoptosis and angiogenesis. This knowledge has direct application to patient stratification and further, to the identification of candidate therapeutic targets and biomarkers intended to monitor OA progression. Conclusion: In light of its high-throughput capabilities and ability to provide comprehensive information on major biological processes, transcriptomics represents a powerful method to support the development of new disease-modifying drugs in OA.