In search of pathobiological endotypes: a systems approach to early rheumatoid arthritis

Therapeutic advances in rheumatoid arthritis

Rheumatoid arthritis (RA) is one of the most common immune mediated inflammatory diseases. People with rheumatoid arthritis present with pain, swelling, and stiffness that typically affects symmetrically distributed small and large joints. Without effective treatment, significant joint damage, disability, and work loss develop, owing to chronic inflammation of the joint lining (synovium). Over the past 25 years, the management of this condition has been revolutionized, resulting in substantially higher levels of disease remission and better long term outcomes. This improvement reflects a paradigm shift towards early and aggressive pharmacological intervention coupled with a proliferation in treatment choice, in turn related to enhanced pathobiological understanding and the advent of new drugs for rheumatoid arthritis. Following an overview of these developments from a historical perspective, and with a general audience in mind, this review focuses on newer, targeted treatments in an ever evolving landscape. The review highlights ongoing areas of debate and unmet need, including the proportion of patients with persistent, difficult-to-treat disease, despite recent advances. Also discussed are personalized, strategic approaches to individual patients, the role for imaging in clinical decision making, and the goal of sustained, drug free remission and disease prevention in the future.

Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications

BACKGROUND

Systemic lupus erythematosus (SLE) is known to be clinically heterogeneous. Previous efforts to characterize subsets of SLE patients based on gene expression analysis have not been reproduced because of small sample sizes or technical problems. The aim of this study was to develop a robust patient stratification system using gene expression profiling to characterize individual lupus patients.

METHODS

We employed gene set variation analysis (GSVA) of informative gene modules to identify molecular endotypes of SLE patients, machine learning (ML) to classify individual patients into molecular subsets, and logistic regression to develop a composite metric estimating the scope of immunologic perturbations. SHapley Additive ExPlanations (SHAP) revealed the impact of specific features on patient sub-setting.

RESULTS

Using five datasets comprising 2183 patients, eight SLE endotypes were identified. Expanded analysis of 3166 samples in 17 datasets revealed that each endotype had unique gene enrichment patterns, but not all endotypes were observed in all datasets. ML algorithms trained on 2183 patients and tested on 983 patients not used to develop the model demonstrated effective classification into one of eight endotypes. SHAP indicated a unique array of features influential in sorting individual samples into each of the endotypes. A composite molecular score was calculated for each patient and significantly correlated with standard laboratory measures. Significant differences in clinical characteristics were associated with different endotypes, with those with the least perturbed transcriptional profile manifesting lower disease severity. The more abnormal endotypes were significantly more likely to experience a severe flare over the subsequent 52 weeks while on standard-of-care medication and specific endotypes were more likely to be clinical responders to the investigational product tested in one clinical trial analyzed (tabalumab).

CONCLUSIONS

Transcriptomic profiling and ML reproducibly separated lupus patients into molecular endotypes with significant differences in clinical features, outcomes, and responsiveness to therapy. Our classification approach using a composite scoring system based on underlying molecular abnormalities has both staging and prognostic relevance.

Association of the serological status of rheumatoid arthritis patients with two circulating protein biomarkers: A useful tool for precision medicine strategies

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation of the joints and presence of systemic autoantibodies, with a great clinical and molecular heterogeneity. Rheumatoid Factor (RF) and anti-citrullinated protein antibodies (ACPA) are routinely used for the diagnosis of RA. However, additional serological markers are needed to improve the clinical management of this disease, allowing for better patient stratification and the desirable application of precision medicine strategies. In the present study, we investigated those systemic molecular changes that are associated with the RF and ACPA status of RA patients. To achieve this objective, we followed a proteomic biomarker pipeline from the discovery phase to validation. First, we performed an iTRAQ-based quantitative proteomic experiment on serum samples from the RA cohort of the Hospital of Santiago de Compostela (CHUS). In this discovery phase, serum samples from the CHUS cohort were pooled according to their RF/ACPA status. Shotgun analysis revealed that, in comparison with the double negative group (RF-/ACPA-), the abundance of 12 proteins was altered in the RF+/ACPA+ pool, 16 in the RF+/ACPA- pool and 10 in the RF-/ACPA+ pool. Vitamin D binding protein and haptoglobin were the unique proteins increased in all the comparisons. For the verification phase, 80 samples from the same cohort were analyzed individually. To this end, we developed a Multiple Reaction Monitoring (MRM) method that was employed in a comprehensive targeted analysis with the aim of verifying the results obtained in the discovery phase. Thirty-one peptides belonging to 12 proteins associated with RF and/or ACPA status were quantified by MRM. In a final validation phase, the serum levels of alpha-1-acid glycoprotein 1 (A1AG1), haptoglobin (HPT) and retinol-binding protein 4 (RET4) were measured by immunoassays in the RA cohort of the Hospital of A Coruña (HUAC). The increase of two of these putative biomarkers in the double seropositive group was validated in 260 patients from this cohort (p = 0.009 A1AG1; p = 0.003 HPT). The increased level of A1AG1 showed association with RF rather than ACPA (p = 0.023), whereas HPT showed association with ACPA rather than RF (p = 0.013). Altogether, this study has allowed a further classification of the RA seropositive patients into two novel clusters: RF+A1AG+ and ACPA+HPT+. The determination of A1AG1 and HPT in serum would provide novel information useful for RA patient stratification, which could facilitate the effective implementation of personalized medicine in routine clinical practice.

The Role of Autoantibody Testing in Modern Personalized Medicine

Personalized medicine (PM) aims individualized approach to prevention, diagnosis, and treatment. Precision Medicine applies the paradigm of PM by defining groups of individuals with akin characteristics. Often the two terms have been used interchangeably. The quest for PM has been advancing for centuries as traditional nosology classification defines groups of clinical conditions with relatively similar prognoses and treatment options. However, any individual is characterized by a unique set of multiple characteristics and therefore the achievement of PM implies the determination of myriad demographic, epidemiological, clinical, laboratory, and imaging parameters. The accelerated identification of numerous biological variables associated with diverse health conditions contributes to the fulfillment of one of the pre-requisites for PM. The advent of multiplex analytical platforms contributes to the determination of thousands of biological parameters using minute amounts of serum or other biological matrixes. Finally, big data analysis and machine learning contribute to the processing and integration of the multiplexed data at the individual level, allowing for the personalized definition of susceptibility, diagnosis, prognosis, prevention, and treatment. Autoantibodies are traditional biomarkers for autoimmune diseases and can contribute to PM in many aspects, including identification of individuals at risk, early diagnosis, disease sub-phenotyping, definition of prognosis, and treatment, as well as monitoring disease activity. Herein we address how autoantibodies can promote PM in autoimmune diseases using the examples of systemic lupus erythematosus, antiphospholipid syndrome, rheumatoid arthritis, Sjögren syndrome, systemic sclerosis, idiopathic inflammatory myopathies, autoimmune hepatitis, primary biliary cholangitis, and autoimmune neurologic diseases.

What is the impact of sex hormones on the pathogenesis of rheumatoid arthritis?

Rheumatoid arthritis (RA) is the most common inflammatory rheumatic disease and has a female predominance of around 3:1. The relationship between sex hormones and RA has been of great interest to researchers ever since Philip Hench's observations in the 1930's regarding spontaneous disease amelioration in pregnancy. Extensive basic scientific work has demonstrated the immunomodulatory actions of sex hormones but this therapeutic potential has not to date resulted in successful clinical trials in RA. Epidemiological data regarding both endogenous and exogenous hormonal factors are inconsistent, but declining estrogen and/or progesterone levels in the menopause and post-partum appear to increase the risk and severity of RA. This review assimilates basic scientific, epidemiological and clinical trial data to provide an overview of the current understanding of the relationship between sex hormones and RA, focusing on estrogen, progesterone and androgens.

ATRPred: A machine learning based tool for clinical decision making of anti-TNF treatment in rheumatoid arthritis patients

Rheumatoid arthritis (RA) is a chronic autoimmune condition, characterised by joint pain, damage and disability, which can be addressed in a high proportion of patients by timely use of targeted biologic treatments. However, the patients, non-responsive to the treatments often suffer from refractoriness of the disease, leading to poor quality of life. Additionally, the biologic treatments are expensive. We obtained plasma samples from N = 144 participants with RA, who were about to commence anti-tumour necrosis factor (anti-TNF) therapy. These samples were sent to Olink Proteomics, Uppsala, Sweden, where proximity extension assays of 4 panels, containing 92 proteins each, were performed. A total of n = 89 samples of patients passed the quality control of anti-TNF treatment response data. The preliminary analysis of plasma protein expression values suggested that the RA population could be divided into two distinct molecular sub-groups (endotypes). However, these broad groups did not predict response to anti-TNF treatment, but were significantly different in terms of gender and their disease activity. We then labelled these patients as responders (n = 60) and non-responders (n = 29) based on the change in disease activity score (DAS) after 6 months of anti-TNF treatment and applied machine learning (ML) with a rigorous 5-fold nested cross-validation scheme to filter 17 proteins that were significantly associated with the treatment response. We have developed a ML based classifier ATRPred (anti-TNF treatment response predictor), which can predict anti-TNF treatment response in RA patients with 81% accuracy, 75% sensitivity and 86% specificity. ATRPred may aid clinicians to direct anti-TNF therapy to patients most likely to receive benefit, thus save cost as well as prevent non-responsive patients from refractory consequences. ATRPred is implemented in R.

Study of Plasma Anti-CD26 Autoantibody Levels in a Cohort of Treatment-Naïve Early Arthritis Patients

In rheumatoid arthritis (RA), the identification of biomarkers to adjust treatment intensity and to correctly diagnose the disease in early stages still constitutes a challenge and, as such, novel biomarkers are needed. We proposed that autoantibodies (aAbs) against CD26 (DPP4) might have both etiological importance and clinical value. Here, we perform a prospective study of the potential diagnostic power of Anti-CD26 aAbs through their quantification in plasmas from 106 treatment-naïve early and undifferentiated AR. Clinical antibodies, Anti-CD26 aAbs, and other disease-related biomarkers were measured in plasmas obtained in the first visit from patients, which were later classified as RA and non-RA according to the American College of Rheumatology criteria. Two different isotype signatures were found among ten groups of patients, one for Anti-CD26 IgA and other for Anti-CD26 IgG and IgM isotypes, both converging in patients with arthritis (RA and Unresolved Undifferentiated Arthritis: UUA), who present elevated levels of all three isotypes. The four UUA patients, unresolved after two years, were ACPA and rheumatic factor (RF) negatives. In the whole cohort, 51.3% of ACPA/RF seronegatives were Anti-CD26 positives, and a similar frequency was observed in the seropositive RA patients. Only weak associations of the three isotypes with ESR, CRP and disease activity parameters were observed. Anti-CD26 aAbs are present in treatment-naïve early arthritis patients, including ACPA and RF seronegative individuals, suggestive of a potential pathogenic and/or biomarker role of Anti-CD26 aAbs in the development of rheumatic diseases.

Inside the Joint of Inflammatory Arthritis Patients: Handling and Processing of Synovial Tissue Biopsies for High Throughput Analysis

Inflammatory arthritis is a chronic systemic autoimmune disease of unknown etiology, which affects the joints. If untreated, these diseases can have a detrimental effect on the patient's quality of life, leading to disabilities, and therefore, exhibit a significant socioeconomic impact and burden. While studies of immune cell populations in arthritis patient's peripheral blood have been informative regarding potential immune cell dysfunction and possible patient stratification, there are considerable limitations in identifying the early events that lead to synovial inflammation. The joint, as the site of inflammation and the local microenvironment, exhibit unique characteristics that contribute to disease pathogenesis. Understanding the contribution of immune and stromal cell interactions within the inflamed joint has been met with several technical challenges. Additionally, the limited availability of synovial tissue biopsies is a key incentive for the utilization of high-throughput techniques in order to maximize information gain. This review aims to provide an overview of key methods and novel techniques that are used in the handling, processing and analysis of synovial tissue biopsies and the potential synergy between these techniques. Herein, we describe the utilization of high dimensionality flow cytometric analysis, single cell RNA sequencing, ex vivo functional assays and non-intrusive metabolic characterization of synovial cells on a single cell level based on fluorescent lifetime imaging microscopy. Additionally, we recommend important points of consideration regarding the effect of different storage and handling techniques on downstream analysis of synovial tissue samples. The introduction of new powerful techniques in the study of synovial tissue inflammation, brings new challenges but importantly, significant opportunities. Implementation of novel approaches will accelerate our path toward understanding of the mechanisms involved in the pathogenesis of inflammatory arthritis and lead to the identification of new avenues of therapeutic intervention.