---
title: "Biomarkers of Treatment Response in Rheumatoid Arthritis: From Conventional Tests to Synovial Immu"
id: "frontiers-in-immunology-0-biomarkers-of-treatment-response-in-rheumatoid-arthritis-from-conventional"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-0-biomarkers-of-treatment-response-in-rheumatoid-arthritis-from-conventional"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1880487"
published_at: "2026-07-17T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Biomarkers of Treatment Response in Rheumatoid Arthritis: From Conventional Tests to Synovial Immu
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/frontiers-in-immunology-0-biomarkers-of-treatment-response-in-rheumatoid-arthritis-from-conventional
- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1880487)
- **Published At:** 2026-07-17T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- Rheumatoid arthritis (RA) is a biologically heterogeneous, immune-mediated disease with variable treatment response; precision selection of therapy requires predictive biomarkers. - This narrative review used a structured literature search (PubMed, Scopus, Web of Science, Google Scholar) covering publications mainly from 2000 to mid‑2026, with updates through 15 June 2026. - The authors integrate **conventional biomarkers** (rheumatoid factor, ACPA, CRP/ESR, calprotectin), pharmacological measures (drug concentrations, anti-drug antibodies), imaging, and multi-omics with **tissue-based** immune phenotyping. - Synovial pathotypes—lympho-myeloid, diffuse-myeloid, and pauci-immune/fibroblastic—reflect distinct cellular programs and may predict mechanism-specific responses. - High-resolution methods (single-cell transcriptomics, spatial transcriptomics, spatial proteomics, immune-repertoire sequencing) identify fibroblast and macrophage subsets, B-cell niches, tertiary lymphoid structures, and ligand–receptor networks that offer mechanistic insight into response and resistance. - Conventional markers remain clinically useful but are incomplete; many patients fail methotrexate or biologic therapy, and treatment selection is often empirical. - The review classifies biomarkers as prognostic versus predictive and further by clinical, pharmacological, molecular, and imaging types, aligned with regulatory frameworks (FDA/EMA BEST resource). - Emphasis is placed on translational readiness: biomarkers should be standardized, reproducible, validated in independent cohorts, and actionable to change management. - The authors argue for integrated biomarker panels combining clinical, pharmacological, molecular, and **synovial tissue** data to enable mechanism-based therapeutic selection. - Key future directions include scalable, externally validated, clinically interpretable models able to assign synovial endotypes and support precision medicine in RA.
## Clinical Analysis & Structured Key Points
About us All journals All articles Submit your research Search Login Frontiers in Immunology Sections Articles Research Topics Editorial board About journal Published in Frontiers in Immunology Autoimmune and Autoinflammatory Disorders : Autoimmune Disorders 7 impact factor 11.3 citescore Part of a Research Topic Prognostic and Predictive Factors in Autoimmune Connective Tissue Disorders -Volume III Submission open 16k views 10 articles Editor & Reviewers Edited by M O Mohammed Osman Reviewed by S F Sukayna Fadlallah S A Shaker Alsharif Outline Abstract 1 Introduction 2 Methods 2 Classification of biomarkers type 3 Drug specific biomarkers 4 Translation of biomarkers into clinical practice 5 Future directions 6 Conclusion Author contributions Funding Conflict of interest Generative AI statement Publisher’s note References Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Table 1 Synovial tissue/molecular subtype and first-line biologic recommendation. View in article Table 2 List of known drug-specific biomarkers. View in article Table 3 Biologic drug selection in RA based on biomarkers types. View in article Table 4 Evidence grading and translational readiness of RA biomarkers. View in article REVIEW article Front. Immunol., 17 July 2026 Sec. Autoimmune and Autoinflammatory Disorders : Autoimmune Disorders Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1880487 Biomarkers of treatment response in rheumatoid arthritis: from conventional markers to tissue immunophenotype N B N.A. Batashkov 1,2 E G E.V. Gerasimova 3 D G D.A. Gerasimova 4 D S D.V. Svetlichnyy 5 E P E.S. Petryakina 5 D T D.I. Tychinin 5 S Y S.M. Yudin 5 V Y V.S. Yudin 5 A K A.A. Keskinov 5 V B V.P. Bogdanov 1,2 E Z E.G. Zotkin 3 A L A.M. Lila 3,6 D T D.V. Tabakov 1,2* M W M. Woroncow 2,7 V S V.I. Skvortsova 8 +7 more P V P.Yu Volchkov 1,2,9* 1. Federal State Budgetary Scientific Institution “Federal Research Center for Innovator and Emerging Biomedical and Pharmaceutical Technologies”, Moscow, Russia 2. Moscow Center for Advanced Studies, Moscow, Russia See more Abstract Rheumatoid arthritis (RA) is a biologically heterogeneous immune-mediated disease characterized by substantial variability in therapeutic response. Despite the availability of multiple conventional synthetic, biologic, and targeted synthetic disease-modifying antirheumatic drugs (DMARDs), many patients fail to achieve adequate disease control or experience secondary loss of efficacy, underscoring the need for predictive biomarkers that can guide treatment selection. This narrative review was based on a structured literature search of PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar, covering publications from January 2000 to June 2026, with earlier landmark studies included when relevant. Literature selection followed PRISMA-informed principles, although the review was not designed as a formal systematic review. Unlike previous reviews that mainly catalogue RA biomarkers by analytical platform, drug class, or clinical use, this review integrates conventional and emerging biomarkers within a tissue-immunophenotype-centered framework. We critically evaluate clinical, serological, pharmacological, molecular, imaging, and tissue-based biomarkers according to biological plausibility, reproducibility, level of validation, clinical actionability, and translational readiness. Established markers such as rheumatoid factor, anti-citrullinated protein antibodies, acute-phase reactants, drug concentrations, and anti-drug antibodies remain clinically useful but provide incomplete insight into mechanism-specific therapeutic response. In contrast, synovial pathotypes, fibroblast and macrophage subsets, B-cell niches, tertiary lymphoid structures, single-cell and spatial omics, and ligand–receptor interaction networks offer a mechanistically richer view of treatment response and resistance. We conclude that precision medicine in RA will require integrated biomarker panels combining clinical, pharmacological, molecular, and synovial tissue data. The key future direction is the development of scalable, externally validated, and clinically interpretable models capable of assigning synovial endotypes and supporting mechanism-based therapeutic selection. 1 Introduction Rheumatoid arthritis (RA) is a chronic systemic immune-mediated inflammatory disease characterized by persistent synovitis, progressive joint destruction, functional disability, and multiple systemic manifestations. RA affects approximately 0.5–1% of the adult population in industrialized countries and is observed two to three times more frequently in women. The incidence increases with age, with peak disease onset typically occurring in individuals over 40 years old (1). Despite substantial progress in therapeutic approaches, RA remains a progressive disease in many patients and continues to represent a major cause of reduced quality of life and long-term disability worldwide (2). RA is highly heterogeneous, particularly at the immunological and molecular levels. Molecular profiling studies of synovial tissue and peripheral blood have demonstrated substantial variability in gene expression patterns, immune cell composition, and cytokine signaling networks between patients (3). Gene-expression analyses have identified distinct synovial pathotypes characterized by different levels of immune activation and stromal remodeling. These include lympho-myeloid, diffuse-myeloid, and pauci-immune/fibroblastic phenotypes, each associated with unique cellular compositions and inflammatory programs (4). Recent advances in single-cell transcriptomics and spatial transcriptomics have further revealed extensive heterogeneity in the synovial microenvironment, identifying diverse populations of T cells, B cells, innate lymphoid cells, macrophages, and fibroblast subsets that play distinct roles in inflammation, tissue damage, and repair (5, 6). This biological heterogeneity has important clinical consequences. Because RA arises from multiple pathogenic mechanisms, treatment strategies are largely based on broad immunosuppressive approaches aimed at reducing inflammation and dampening immune activation. Current treatment guidelines recommend conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) as first-line therapy, with methotrexate serving as the anchor drug in most treatment regimens. In patients who fail to achieve adequate disease control, therapy is typically escalated to biologic DMARDs targeting key inflammatory pathways such as tumor necrosis factor (TNF), interleukin-6 (IL-6), B cells (anti-CD20 therapy), or T-cell co-stimulation, as well as targeted synthetic DMARDs, including Janus kinase (JAK) inhibitors (7, 8). Despite the availability of multiple therapeutic options, treatment response in RA remains highly variable. Approximately 30–40% of patients do not achieve adequate clinical response to first-line methotrexate therapy, and up to 40–50% of patients exhibit insufficient response or secondary loss of response to biologic therapies (9, 10). Currently, treatment selection is largely empirical, and reliable predictive biomarkers capable of guiding therapeutic choice are lacking. As a result, many patients undergo multiple sequential treatment switches before achieving disease control, which may lead to prolonged inflammation and irreversible joint damage. In recent years, rapid progress in high-throughput molecular technologies—including single-cell transcriptomics, immune repertoire sequencing, spatial transcriptomics, and advanced proteomics-has significantly expanded the ability to characterize the cellular and molecular architecture of rheumatoid arthritis (5, 6). This technological shift has been accompanied by a growing number of recent review articles synthesizing emerging data on RA pathogenesis, molecular stratification, and biomarker discovery (11–13). Several recent reviews have summarized biomarkers in RA, including traditional serological markers, pharmacological monitoring, imaging biomarkers, multi-omics approaches, and machine-learning-based precision medicine strategies. However, many of these works primarily organize biomarkers according to analytical platform, clinical use, or drug class, and therefore provide limited integration between biomarker evidence and the cellular architecture of the inflamed synovium. In recent years, in light of the development and spread of approaches to therapy aimed at the functioning of immune cells, there has been a growing interest in finding biomarkers related to the state of immune cells before and during treatment. The present review tries to consider new microenvironment biomarkers in context with other well-studied classical ones. Rather than treating biomarkers as isolated circulating analytes or descriptive molecular signatures, we emphasize how synovial pathotypes, fibroblast and macrophage subsets, B-cell niches, tertiary lymphoid structures, spatial transcriptomics, single-cell RNA sequencing, spatial proteomics, and ligand–receptor interaction networks define disease mechanisms that may influence response or resistance to specific therapies. In this framework, conventional biomarkers such as RF, ACPA, CRP, calprotectin, drug levels, and anti-drug antibodies are discussed as clinically useful but incomplete readouts, whereas tissue-based biomarkers are presented as a mechanistically richer layer for patient stratification. Thus, the novelty of this review lies in integrating classical and emerging biomarkers into a tissue-centered model of RA precision medicine, in which therapeutic selection is guided not only by systemic inflammation or serological status, but by the dominant cellular circuits operating within the synovial microenvironment. In this context, the present review aims to address this gap by focusing specifically on biomarkers of treatment response that are supported by mechanistic insights derived from high-resolution approaches, including single-cell and immune repertoire analyses. By linking therapeutic outcomes to cellular states, immune pathways, and clonal dynamics within the synovial microenvironment, this work seeks to provide a more precise framework for biomarker development and patient stratification in RA. In particular, emerging evidence suggests that immune phenotyping of synovial tissue, including characterization of immune cell populations, B-cell receptor and T-cell receptor repertoires, and fibroblast-like synoviocyte phenotypes, may represent especially promising directions for biomarker discovery. Understanding how these molecular and cellular features influence treatment response could enable the development of precision medicine approaches in RA. In this review, we summarize the current state of knowledge regarding biomarkers predicting therapeutic response in rheumatoid arthritis, with a focus on both conventional and targeted therapies. We also discuss emerging technologies and conceptual frameworks that may help guide future biomarker discovery and enable more personalized treatment strategies for RA. 2 Methods The literature search was conducted to identify studies investigating biomarkers of therapeutic response in rheumatoid arthritis, with particular emphasis on biomarkers supported by mechanistic evidence and studies exploring tissue-level and immune-cell–specific determinants of treatment response. The initial literature search was performed between January 2026 and May 2026. The search was last updated on 15 June 2026 to include the most recent publications relevant to synovial pathotypes, single-cell transcriptomics, spatial transcriptomics, spatial proteomics, and immune-cell interaction networks. The following electronic databases were searched: PubMed/MEDLINE. Scopus. Web of Science. Google Scholar (for additional records and citation tracking). Reference lists of relevant reviews and original articles were also manually screened to identify additional publications. Search queries combined terms related to rheumatoid arthritis, biomarkers, treatment response, and specific therapeutic mechanisms. Examples of search terms included: “rheumatoid arthritis” AND biomarker*. “rheumatoid arthritis” AND treatment response. “predictive biomarkers” AND rheumatoid arthritis. “prognostic biomarkers” AND rheumatoid arthritis. “methotrexate” AND biomarker. “TNF inhibitor” AND biomarker “rituximab” AND biomarker. “tocilizumab” AND biomarker. “abatacept” AND biomarker. “JAK inhibitor” AND biomarker. “synovial tissue” AND rheumatoid arthritis. “synovial pathotype”. “single-cell transcriptomics” AND rheumatoid arthritis. “immune repertoire” OR “AIRR-seq” AND rheumatoid arthritis. “precision medicine” AND rheumatoid arthritis. The primary search included studies published between January 2000 and March 2025. Earlier landmark studies were included when they represented foundational work on established biomarkers (for example methotrexate pharmacogenetics). Studies were considered eligible if they follow at least one of these inclusion criteria: investigated biomarkers associated with therapeutic response, resistance, disease activity, or prognosis in RA; evaluated conventional synthetic, biologic, or targeted synthetic DMARDs; included clinical, serological, pharmacological, molecular, transcriptomic, proteomic, imaging, or tissue-based biomarkers; provided mechanistic insight into biomarker function or biological relevance; were original research articles, meta-analyses, systematic reviews, or high-quality narrative reviews; were published in peer-reviewed journals; were available in English. Studies were excluded if they: did not focus on rheumatoid arthritis; investigated biomarkers unrelated to disease activity or therapeutic outcomes; were conference abstracts without full-text publication; were case reports or very small exploratory studies lacking sufficient methodological detail; contained insufficient clinical or biological data for interpretation; were duplicate publications. Titles and abstracts retrieved through the database search were screened for relevance. Full-text articles were subsequently evaluated for eligibility according to the predefined inclusion and exclusion criteria. Particular attention was given to studies providing: mechanistic evidence linking biomarkers to specific therapeutic pathways; validation in independent cohorts; prospective evaluation of predictive performance; tissue-based, single-cell, or immune-repertoire analyses. Priority was assigned to meta-analyses, systematic reviews, prospective cohorts, randomized clinical trials, and large multicenter studies. This review was designed as a narrative review with a structured literature search rather than a formal systematic review. Therefore, a full PRISMA flow diagram was not generated. However, literature identification, screening, eligibility assessment, and study selection were performed according to PRISMA-informed principles to improve transparency and minimize selection bias. 2 Classification of biomarkers type Biomarkers represent key tools of precision medicine, enabling the characterization of disease processes at the biological level. A biomarker is defined as a measurable indicator of normal biological processes, pathogenic mechanisms, or pharmacological responses to therapeutic interventions (14). In RA, biomarkers are most usefully classified according to their clinical function, particularly their ability to inform prognosis or guide therapeutic decision-making. From this perspective, biomarkers can be broadly divided into prognostic and predictive categories. Prognostic biomarkers provide information about the expected course or outcome of disease independently of treatment. In RA, such markers may include clinical characteristics, serological autoantibodies, inflammatory markers, imaging findings, and molecular signatures associated with structural progression or functional decline (9). These biomarkers help identify patients at higher risk of aggressive disease and may influence treatment intensity or monitoring strategies (Figure 1). Figure 1 Known biomarkers for rheumatoid arthritis treatment. In contrast, predictive biomarkers are used to estimate the likelihood of response to a specific therapeutic intervention. Predictive biomarkers are of particular importance in RA because treatment responses vary widely even among patients receiving the same drug. These markers often reflect the activity of specific pathogenic pathways targeted by therapy, such as TNF-driven inflammation, B-cell–mediated autoimmunity, T-cell activation, or IL-6 signaling. Identification of reliable predictive biomarkers is therefore essential for improving treatment stratification and advancing precision medicine approaches in RA. Within the predictive biomarker category, further subclassification can be made based on the type of biological information they provide. This includes clinical biomarkers, pharmacological biomarkers, molecular biomarkers, and imaging biomarkers reflecting synovial inflammation. Each of these biomarker types offers distinct advantages and limitations in terms of feasibility, specificity, and clinical applicability (Figure 2). Figure 2 Biomarkers classification in RA. For biomarkers to be clinically useful, they must be standardized, reproducible, and actionable, meaning that their measurement should lead to meaningful changes in clinical decision-making, such as treatment selection, dose optimization, or toxicity prevention (15). In the following sections, we review current evidence on biomarkers of therapeutic response in RA, structured according to their clinical relevance and biological level, including clinical, pharmacological, molecular, and imaging predictors of response. This framework is aligned with widely accepted regulatory classifications proposed by the U.S. Food and Drug Administration and European Medicines Agency, which categorize biomarkers based on their clinical function, including diagnostic, prognostic, predictive, monitoring, pharmacodynamic, and safety biomarkers. Within this framework, predictive and prognostic biomarkers represent key categories for precision medicine in RA, providing the conceptual basis for treatment stratification and outcome assessment. These definitions are further formalized in the BEST (Biomarkers, EndpointS, and other Tools) resource developed by the FDA-NIH Biomarker Working Group. 2.1 Clinical immune biomarkers Clinical biomarkers are routinely collected measures that quantify current inflammatory burden, functional impact, and structural risk. They are widely used because of accessibility and established thresholds. In RA, clinical “tools” are used to indirectly assess signs of inflammation - pain and swelling of the joints, but not the actual intensity of the inflammatory lesion of the synovial membrane and internal organs. Calculating the painful and swollen of the joints is characterized by relatively low reproducibility, does not allow for the detection of subclinical joint inflammation, and does not take into account other possible manifestations of rheumatoid inflammation (tenosynovitis, fibromyalgia, etc.) (16). 2.1.1 Inflammatory markers and composite activity indices Disease activity in rheumatoid arthritis is commonly assessed using composite clinical indices. One of the most widely used is the Disease Activity Score (DAS), particularly its modified version DAS28, which integrates the number of tender and swollen joints (out of 28 assessed joints), the erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP) levels as markers of systemic inflammation, and the patient’s global assessment (PGA) of disease activity. However, DAS values are high
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