MolDX: Molecular Biomarker Testing to Guide Targeted Therapy Selection in Rheumatoid Arthritis

Title XVIII of the Social Security Act, §1862(a)(1)(A) allows coverage and payment for only those services that are considered to be reasonable and necessary

42 CFR §410.32(a) Diagnostic x-ray tests, diagnostic laboratory tests, and other diagnostic tests: Conditions

CMS Internet-Only Manual, Pub. 100-02, Medicare Policy Manual, Chapter 15, §80 Requirements for Diagnostic X-Ray, Diagnostic Laboratory, and Other Diagnostic Tests, §80.1.1 Certification Changes

Current molecular biomarker tests to guide targeted therapy selection in Rheumatoid Arthritis (RA) are non-covered by this contractor.

Rheumatoid arthritis (RA) is complex and heterogeneous inflammatory autoimmune disease, with a multifactorial etiology.^1-3 An estimated 1.3 million adults in the United States live with RA and though the disease affects both sexes, the incidence is higher in women than in men (53/100,000 vs 29/100,000 population).⁴ Left improperly treated, it can progress and become a debilitating disease with significant morbidity as well as increased mortality.^2,5,6

RA treatment response is defined in terms of disease activity or remission scores. Commonly used are the American College of Rheumatology (ACR) and the European League Against Rheumatism (EULAR) response criteria.^7-10 EULAR response criteria are based on changes in the Disease Activity Score (DAS), while the ACR improvement scores of ACR20, ACR50, and ACR70, represent the percent improvement in a standard set of indices, including clinical factors as well as laboratory markers (i.e. acute-phase reactants).^11,12 A 50% response (ACR50) is needed for most patients to reach low disease activity.³ The ACR and EULAR criteria have been reported to have comparable validity.¹³ However, given that their components and requirements are different, and that there is known variability in patient disease assessments (PDAs) and patient-reported outcome measures (PROMs) as well as in the composite of clinical metrics used that are included in the response criteria, some patients who are classified as responders by one criterion may not be classified as responders by another criterion.^9,14-16 For example, differences are seen among the various EULAR response criteria, depending on which of the composite measures is used (i.e. disease activity score using 28 joint counts (DAS28)/Erythrocyte Sedimentation Rate (ESR) vs DAS28/C-reactive protein (CRP)).^17,18Other scores, namely the clinical disease activity index (CDAI), and the simplified disease activity index (SDAI), are also commonly used in clinical care and correlate with outcomes such as progression and functional impairment.¹⁹

Conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) such as methotrexate are considered first-line therapy for many patients with RA.²⁰ However, up to 60% of patients fail to achieve treatment targets on csDMADRs.²¹ For them, biologic and targeted synthetic DMARDs (b/t DMARDs), are often recommended.^20,22 These include tumor necrosis factor-alpha inhibitors (anti-TNFs, or TNFis), Janus Kinase tyrosine kinases (JAKs), Interleukin-6 (Il-6) inhibitors, T- and B-cell therapies. The various t/bDMARDs have, on the whole, shown similar efficacy and safety profiles; as such, many guidelines have not preferred or prioritized among them.^20,23,24 Despite the fact that there are multiple classes of targeted therapies available, TNFis remain the predominant first-line b/tDMARDs in the majority of patients with RA for a variety of reasons, including long-term established efficacy and safety profiles, the comfort level of ordering physicians, and insurance policy requirements.^25-29 However, up to two-thirds of patients will fail to achieve ACR50 within 6 months of therapy with their first TNFi, and more than 60% will require a third DMARD.^19,30,31 After patients fail their first TNFi therapy, they are approximately 12-30% more likely to inadequately respond to their second targeted medication.^32-35 Over time, up to 75% of patients may eventually reach treatment targets.¹⁹

When TNFi therapy is ineffective or results in adverse effects, patients may ‘cycle’ to another TNFi or to ‘switch’ to a drug with a different mechanism of action (MOA). The ACR conditionally recommends ‘switching’ over ‘cycling,’ based on evidence supporting greater improvement in disease activity when patients switch drug classes, rather than cycle between them.^20,32,36-38 However, a claims analysis-based study found that most (~64%) patients still often cycle to a second TNFi before switching to a non-TNFi, and that patients who switch to a non-TNFi are significantly older with more comorbidities.³⁹ These trial-and-error attempts using various drugs and drug classes, often with inadequate disease response and with drug-induced side-effects, can result in continued disease progression, as effective therapy (which is important early in RA to delay or prevent debilitating disease outcomes) is delayed.^5,20

Despite the availability of multiple treatment options, there is no certain way to predict which patients will respond to the various available therapies. Certain characteristics, including obesity and sex, have been associated with a lack of response to TNFis in some studies but not in others.^40-44 Moreover, up to 30% of RA patients do not have rheumatoid factor (RF) or anti-citrullinated protein antibodies (ACPA), and researchers remain divided on whether to subdivide and manage RA differently based on autoantibody status.^45,46 For example, there have been conflicting reports regarding whether seronegative patients have less active disease at baseline and less radiographic progression than seropositive patients.^46-49 Moreover, distinct genetic factors have been associated with seronegative patients, indicating that there may be distinct pathogenic mechanisms involved.^50,51 Further, studies have found that treatment choice is influenced by the presence of autoantibodies and that patients with autoantibodies may require biologics to achieve remission more frequently than seronegative patients.^47,52 Additionally, in some multivariable analyses, RF has been negatively associated with biologic therapy survival and ACPA with the inability to taper or discontinue TNFis after remission.³⁷ However, other studies, including meta-analyses, have not found these associations; rather, they report that both seropositive and seronegative types of RA may require similar intensive treat-to-target therapies.^49,53-55 Differences in study findings may be due to different classification schemes used (i.e. RA diagnosed using ACR 2010 vs older criteria) as well as on the inclusion PDAs and PROMs, which are varied and prone to inherent subjectivity. Nevertheless, there remain unanswered questions regarding the management of different sub-populations with RA. As such, there is an unmet clinical need for a test that can accurately predict which patients will or will not respond to targeted and biologic therapies.

Predictive Biomarker Tests

A number of molecular biomarker tests have been proposed that can predict response (or non-response) to certain classes or multiple classes of drugs in RA treatment.^56-63 Some are based on genetic markers found in blood, some on genetic markers in combination with clinical and laboratory factors, and others on transcriptomics within the synovium. One observational cohort evaluated the differential expression and methylation of DNA to predict response to two different TNFi drugs adalimumab (ADA) and etanercept (ETN) in patients with RA. A machine learning model generated found divergent transcriptomic signatures in ADA and ETN responders that predicted drug response with an accuracy of up to 85-88%.⁵⁷ Interestingly, in that study the majority of the patients did not respond to both drugs but had the potential to respond to either ADA or ETN. However, a prior study evaluating DNA methylation in RA found different methylation sites than those reported by Tao et al.⁶⁴ The differences may have been due to the different cell types interrogated as well as different response criteria used. However, these studies were limited by small sample sizes and have not been externally validated using larger sample sizes.

The above-mentioned studies evaluated a decision regarding the choice of TNFi (ADA vs ETN). Other biomarker tests have been proposed to assist in the decision regarding which class of drug to use – i.e. whether to use a TNFi versus a drug with an alternate mechanism of action (alt-MOA, or non-TNFi). One such test is a molecular signature response classifier (MSRC) that can be performed prior to the start of targeted or biologic therapy to predict non-response (NR) to TNFis.^59,65 This test is performed on whole blood and includes 10 single-nucleotide polymorphisms (SNPs) associated with RA, 8 gene transcripts, 2 traditional laboratory tests (CRP and anti-CCP), and 3 clinical parameters (sex, body mass index (BMI), and patient disease assessment (PDA)).^52,59 The raw output of the model is transformed into a continuous variable between 1 and 25 with higher numbers indicating a greater likelihood of non-response to TNFis.⁶⁶

In a clinical validity study of 175 prospectively collected samples from RA patients in the CERTAIN trial, the top 23 ranked biomarkers were found to identify ACR50 non-responders with a sensitivity of 50.0%, specificity of 86.8%, and a positive predictive value (PPV) of 89.7%.^59,67 The overall TNFi response rate was 30.3%, whereas patients predicted to be non-responders (NRs) by the test had an ACR50 response rate of 10.3% (7/68) with TNFi therapies.⁵⁹ Conversely, lack of a NR signature did not predict response, as nearly 60% of patients with this result did not meet ACR50 response criteria when treated with a TNFi.⁵⁹ Another prospective clinical study (NETWORK-004) also reported that patients with a molecular signature of NR were less likely to achieve therapy targets with TNFis than those lacking the signature with odds ratios (ORs) of 3.4–8.8 for b/tDMARD-naive (n=146) and 3.3–26.6 for TNFi-exposed patients (n=113) (notably, the OR of 26 was in previously TNFi-exposed patients).⁶⁵ In this study, a NR signature was detected in nearly 45% of patients at baseline.⁶⁵ Moreover, logistic regression performed using the scores from the molecular and clinical features as two individual independent components of the MSRC found both to be statistically significant and, on this basis, the authors concluded that the clinical and genomic components of the MSRC carry ‘complementary’ information.⁶⁵ Importantly, both of these studies were observational and test results were not actually used to inform treatment selection. A prospective multi-institutional cohort study using a clinical database of RA patients (Study to Accelerate Information of Molecular Signatures [AIMS] in Rheumatoid Arthritis) evaluated outcomes in patients for who a b/tsDMARD treatment decision was informed by MSRC testing.⁶⁸ The primary endpoint was therapeutic responsiveness defined by achievement of ACR50 at 24-weeks. According to questionnaire responses, therapy selection was informed by the test results for 73.5% (277/377) of patients.⁶⁸ However, only 85 patients completed a 24-week follow-up visit. Patient responses to treatment (informed by the MSRC) at 24 weeks in predicted non-responders who received an alternate drug (alt-MOA) (n=23 patients) and in predicted non-responders who received a TNFi despite their NR signature (n=29 patients) were 34.8% and 10.3% by ACR50.⁶⁸ Response to TNFis in patients lacking the non-response signature was only 45%.⁶⁸ Patients with and without a molecular signature of NR who received an alt-MOA had a nearly equivalent responses (33-34%) by ACR50.⁶⁸  Notably, the number of evaluable patients at 24 weeks in each sub-group was small and despite access to the test results, more patients with predicted NR to TNFis were still prescribed TNFis rather than alt-MOAs.

Other predictive biomarker tests are ‘biopsy-driven,’ evaluating the transcriptomic signature of the synovium in RA patients as an indicator for therapy response and clinical outcomes.^69,70 R4RA (rituximab vs tocilizumab in anti-TNF inadequate responder patients with rheumatoid arthritis) was a multicenter randomized trial evaluating RA patients with inadequate responses to TNFis.⁶⁹ RNA sequencing-based identification of patients with a low or absent B cell gene expression signature in synovial tissue significantly correlated with a greater response to tocilizumab (63%) than rituximab (36%), suggesting that in patients with a low or absent B cell expression gene signature in synovial tissue, an alternative therapy may be preferred over rituximab.⁶⁹ Another longitudinal study of the synovial transcriptome reported differentially expressed genes in DMARD-naïve early-RA patients versus advanced RA patients.⁷⁰ Results from the synovial transcriptome studies have not yet been replicated in external validation cohorts. Moreover, studies have shown pronounced heterogeneity in the synovial tissue of RA patients, mirroring the general heterogeneity seen in this inflammatory disease.⁷¹ Finally, synovial fluid is not readily available for testing to inform therapy decisions in routine clinical practice settings. Nonetheless, it remains an active area of exploration for both the pathogenesis as well as the response to therapy in RA.

Though various candidate polymorphisms have been proposed to be associated with TNFi treatment response in RA patients, multiple genome-wide association studies (GWAS) have not identified such predictive genetic variants in a consistent or reproducible manner.⁷² An ‘open challenge’ comparing prediction models developed by 73 research groups found that, despite a “genetic heritability estimate of treatment non-response trait,” SNP data do not significantly contribute to the prediction of response to therapy above that which was obtained by available clinical predictors.⁷³ Specifically, this analysis did find that certain available clinical features, including sex, age, the specific TNFi, and methotrexate use, did provide a level of prediction that performed significantly better than random.⁷³ The authors conclude that “these results suggest that future research efforts focused on the incorporation of a richer set of clinical information—including seropositivity, treatment compliance and disease duration—may provide opportunity to leverage these methods in clinically meaningful ways. In addition, the identification of data modalities that are more effective than genetics in capturing heterogeneity in RA disease progression—whether clinical, molecular or other—may also improve predictive performance.”⁷³

Drug treatments themselves have been reported to alter the molecular profile of RA patients, both in terms of normalization of the profile (which has been associated with clinical remission), and in terms of resistance to treatments.^60,74 A multi-omics analysis found that normalization and resistance are also heterogeneous, which may be explained by an imbalance of white blood cell subsets. It remains unclear, however, whether these signatures are also found in the inflamed synovium.⁶⁰

Finally, the EULAR and others have reported (and cautioned) on the variability inherent in the existing big data sources, on the tolerance of poor quality data in large registries used in RA biomarker studies, on the heterogeneous methods used between studies to analyze big data, and on the lack of external validation of some of the tests.^58,75-77 These issues lend themselves to the risks of ‘quantitative fallacy,’ bias, overfitting of predictive models, and the inability to generalize results.^75,78 EULAR has called for comprehensive and harmonized standards, open data platforms, and interdisciplinary collaboration, such that artificial intelligence (AI) can safely and effectively be implemented in the clinical practice of RA.⁷⁶

Contractor Advisory Committee (CAC)

A Contractor Advisory Committee (CAC) meeting on the topic of predictive testing in RA was held in December 2021. Similar to findings from a published survey of 248 USA-based rheumatologists, the CAC subject matter expert (SME) panelists noted that physicians would welcome predictive tests to guide targeted therapy in RA patients and find them useful if they could help minimize the trial-and-error approach of current therapy.⁷⁹ However, the panelists were not aware that any such tests have been rigorously validated for routine clinical use at the present time. Additionally, the panelists expressed the opinion that TNFis are the most commonly prescribed biologic therapies in RA for two primary reasons – (1) they were the first biologics available for RA patients and therefore physician and patient comfort levels may be higher with this class of drug over ‘newer’ therapies with similar safety and efficacy profiles and (2) insurance companies often require a trial of 1-2 TNFis before covering other targeted therapies. There was consensus that the requirement by insurance companies for patients to fail multiple TNFis prior to paying for an alternate targeted therapy is unreasonable.

Finally, international, national, and society guidelines have not endorsed predictive (for response to therapy) biomarker testing to-date, and the members of the CAC committee, while agreeing in the value for such tests, do not think such a test has yet been demonstrated to warrant routine clinical use.

TNFi therapies are often the first biologic therapy used to treat RA patients who have not responded to csDMARDs. However, many patients do not adequately respond to TNFis. Therefore, a test that can accurately predict response would help inform therapeutic decision-making, lessen unnecessary trial-and-error approaches, eliminate or reduce the time to effective therapy, and improve patient outcomes.

Multiple studies have pointed to the presence of a molecular signature underlying the pathophysiology of TNFi response in RA. However, RA is a heterogeneous disease in terms of pathogenesis as well as treatment response and no genomic signature has, to-date, robustly predicted therapy response demonstrably beyond the capabilities of established clinical and biological factors. Other baseline patient characteristics such as obesity, female sex, and the presence of RF, have repeatedly been associated with a lack of response to TNFi therapy.^42,80,81 In published studies, these factors may have impacted observed treatment responses as well as provider treatment decisions independently of any test result. However, the MSRC studies evaluated did not provide results from a multivariable analysis to establish that the genomic component of the test adds demonstrable value above and beyond that provided by the combination of available clinical, demographic and (traditional) laboratory parameters. It is therefore unclear whether biomarker tests can outperform the combination of clinical and other factors in predicting who will respond to TNFi therapy, particularly when those variables are also included as components of the tests themselves (as is the case with the MSRC).

Further, physician and patient assessments of disease activity can be considerably variable. For example, the DAS28-CRP has been reported to significantly underestimate disease activity and overestimate improvements compared with DAS28-ESR.⁸² It is imperative, therefore, that any biomarker test is clinically validated according to multiple validated response outcome criteria (including ACR50, DAS28-CRP and CDAI) in order to perform as a robust predictor.^59,65,66 Moreover, PDAs and PROMS are subjective, have inherent variability, and are not well-defined in the tests that use them as components. They also lack important measures of disease assessment. For example, a systematic review found a lack of adequate PROMs to evaluate and define symptoms such as stiffness and sleep problems, both common in RA.^15,16 Further, it is unclear how patients with various levels of symptoms, flares, and periods of remission were accounted for in studies that included PDAs in their analyses. Finally, these disease assessments themselves have been reported to differentiate TNFi responders from non-responders.⁸³ As such, the extent of impact from all of these variables on claimed test accuracy across the published studies is as-yet unknown, and any discordance observed in the various disease activity measures and PROMS must be thoroughly investigated, rather than eliminating such results from analysis, as has been done in some of the published literature.⁶⁶ In fact, researchers have called for agreed prioritization across stakeholders about the most important metrics to collect for clinical decision-making and in clinical trials, and the ACR recently provided recommendations for Functional Assessment Instruments in RA suitable for routine clinical use.^16,84,85

Some of the biomarker tests consider responses to TNFis as a class but it is not clear whether only one or a few drugs within the class account for most of the impact observed regarding disease response. It is also unknown whether certain variables impact certain TNFis more than others within the class. For example, one study found that the most important clinical factors associated with TNFi response were age for adalimumab, rheumatoid factor for etanercept, erythrocyte sedimentation rate for infliximab and golimumab, disease duration for abatacept, and C-reactive protein for tocilizumab.⁸⁶ Finally, as some of the tests do not discriminate between TNFi agents, patients may be considered non-responders to the entire class of TNFis when in fact, the NR signature may be heavily skewed toward a specific TNFi, unnecessarily limiting patients’ treatment options.

It is evident that limited sample sizes, the complexity of gene expression data analysis, the uncertain combinatorial effects of serologic and other laboratory markers with clinical variables, and the variability in validated disease assessments and in patient reported assessments of disease activity have all proven to be challenges in the development of robust predictive biomarker tests that are generalizable across RA patient cohorts.^78,87 Moreover, many of the clinical utility studies are observational or based on modeling, with test results not used to inform therapy selection in a real-world clinical setting. In those studies that have used biomarker tests to inform therapy, it is not clear that therapeutic choices were, in fact, a direct result of the test. Further, patient outcomes should be improved as a result of the test informing choice of therapy; however, this has not been evident to-date. For example, in the MSRC studies, the response to alt-MOAs in the predicted NR group should have exceeded that observed with TNFis at baseline, but it did not. There were also numerous additional limitations in those studies may have significantly impacted treatment responses, including the following:

• Patient eligibility did not consider baseline disease activity, prior biological exposure, or csDMARD use;
• Patients with conflicting information in the AIMS database relative to primary source documents were excluded without further investigation;
• Statistically significant differences in responses to therapy were observed for age, sex, disease duration and baseline prednisone use but were not otherwise accounted for in a multivariable analysis; 
• Neither patients nor providers were blinded to the MSRC.

Finally, many patients who are not predicted to be non-responders to TNFis will still fail to achieve an adequate response to TNFi therapy for reasons that may be unrelated to their molecular signature, such as the development of anti-drug antibodies, which have been reported for all TNFi therapies and are known to decrease TNFi response in RA.⁸⁸ Therefore, a clear benefit of testing has not been demonstrated in predicted responders nor in predicted non-responders to TNFis.

On a final note, biomarker tests should not be used to shroud other deficiencies in the healthcare delivery system. While this contractor does not believe it appropriate that patients continue classes of therapy that they have already failed due to payor preference, it is also not appropriate to perform unnecessary testing to avoid such preferences that are not grounded in evidence.

In conclusion, although there is apparent need and clinical utility for identifying appropriate 2^nd line therapy with biologics in RA patients, the current evaluated tests that measure genetic and gene expression factors have not yet demonstrated definitive value above the combination of available clinical, laboratory, and demographic data, which are also known to provide some predictive information regarding response to TNFis. Therefore, clinical validity has not yet been established for molecular biomarker tests that guide targeted therapy selection in RA. This contractor will continue to monitor the evidence and may modify coverage based on new information in the pertinent literature and society recommendations.

1. Lopez-Pedrera C, Barbarroja N, Patiño-Trives AM, et al. Effects of biological therapies on molecular features of rheumatoid arthritis. International Journal of Molecular Sciences. 2020;21(23):9067.
2. Centers for Disease Control and Prevention. Rheumatoid Arthritis (ra). 2020; https://www.Cdc.Gov/arthritis/basics/rheumatoid-arthritis.Html.Accessed July 7, 2022.
3. Smolen JS, Aletaha D, McInnes IB. Rheumatoid arthritis. Lancet (London, England). 2016;388(10055):2023-2038.
4. Myasoedova E, Davis J, Matteson EL, Crowson CS. Is the epidemiology of rheumatoid arthritis changing? Results from a population-based incidence study, 1985-2014. Annals of the Rheumatic Diseases. 2020;79(4):440-444.
5. Listing J, Kekow J, Manger B, et al. Mortality in rheumatoid arthritis: The impact of disease activity, treatment with glucocorticoids, tnfα inhibitors and rituximab. Annals of the Rheumatic Diseases. 2015;74(2):415-421.
6. Monti S, Montecucco C, Bugatti S, Caporali R. Rheumatoid arthritis treatment: The earlier the better to prevent joint damage. RMD Open. 2015;1(Suppl 1):e000057-e000057.
7. England BR, Tiong BK, Bergman MJ, et al. 2019 update of the American College of Rheumatology recommended rheumatoid arthritis disease activity measures. Arthritis Care & Research. 2019;71(12):1540-1555.
8. Felson DT, Smolen JS, Wells G, et al. American college of Rheumatology/European League Against Rheumatism provisional definition of remission in rheumatoid arthritis for clinical trials. Arthritis and Rheumatism. 2011;63(3):573-586.
9. Bykerk VP, Massarotti EM. The new ACR/EULAR remission criteria: Rationale for developing new criteria for remission. Rheumatology. 2012;51(suppl_6):vi16-vi20.
10. Kay J, Upchurch KS. ACR/EULAR 2010 rheumatoid arthritis classification criteria. Rheumatology. 2012;51(suppl_6):vi5-vi9.
11. Felson DT, Anderson JJ, Boers M, et al. American College of Rheumatology. Preliminary definition of improvement in rheumatoid arthritis. Arthritis and Rheumatism. 1995;38(6):727-735.
12. Hobbs KF, Cohen MD. Rheumatoid arthritis disease measurement: A new old idea. Rheumatology. 2012;51(suppl_6):vi21-vi27.
13. van Gestel AM, Anderson JJ, van Riel PL, et al. ACR and EULAR improvement criteria have comparable validity in rheumatoid arthritis trials. American College of Rheumatology European League of Associations for Rheumatology. J Rheumatol. 1999;26(3):705-711.
14. Ward MM, Guthrie LC, Alba MI, Dasgupta A. Origins of discordant responses among 3 rheumatoid arthritis improvement criteria. J Rheumatol. 2018;45(6):745-752.
15. Küçükdeveci AA, Elhan AH, Erdogan BD, et al. Use and detailed metric properties of patient-reported outcome measures for rheumatoid arthritis: A systematic review covering two decades. RMD Open. 2021;7(2).
16. Gossec L, Dougados M, Dixon W. Patient-reported outcomes as end points in clinical trials in rheumatoid arthritis. RMD Open. 2015;1(1):e000019-e000019.
17. Ranganath VK, Yoon J, Khanna D, et al. Comparison of composite measures of disease activity in an early seropositive rheumatoid arthritis cohort. Annals of the Rheumatic Diseases. 2007;66(12):1633-1640.
18. Fleischmann RM, van der Heijde D, Gardiner PV, Szumski A, Marshall L, Bananis E. Das28-crp and das28-esr cut-offs for high disease activity in rheumatoid arthritis are not interchangeable. RMD Open. 2017;3(1):e000382.
19. Aletaha D, Smolen JS. Diagnosis and management of rheumatoid arthritis: A review. Jama. 2018;320(13):1360-1372.
20. Fraenkel L, Bathon JM, England BR, et al. 2021 American College of Rheumatology guideline for the treatment of rheumatoid arthritis. Arthritis care & Research. 2021;73(7):924-939.
21. Emery P, Horton S, Dumitru RB, et al. Pragmatic randomised controlled trial of very early etanercept and mtx versus mtx with delayed etanercept in ra: The vedera trial. Annals of the Rheumatic Diseases. 2020;79(4):464-471.
22. Kerschbaumer A, Sepriano A, Smolen JS, et al. Efficacy of pharmacological treatment in rheumatoid arthritis: A systematic literature research informing the 2019 update of the EULAR recommendations for management of rheumatoid arthritis. Annals of the Rheumatic Diseases. 2020;79(6):744-759.
23. Mian A, Ibrahim F, Scott DL. A systematic review of guidelines for managing rheumatoid arthritis. BMC Rheumatol. 2019;3:42.
24. Smolen JS, Landewé RBM, Bijlsma JWJ, et al. EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2019 update. Annals of the Rheumatic Diseases. 2020;79(6):685-699.
25. Evangelatos G, Bamias G, Kitas GD, Kollias G, Sfikakis PP. The second decade of anti-tnf-a therapy in clinical practice: New lessons and future directions in the covid-19 era. Rheumatology International. 2022:1-19.
26. Curtis JR, Zhang J, Xie F, et al. Use of oral and subcutaneous methotrexate in rheumatoid arthritis patients in the United States. Arthritis Care & Research. 2014;66(11):1604-1611.
27. Solomon DH, Xu C, Collins J, et al. The sequence of disease-modifying anti-rheumatic drugs: Pathways to and predictors of tocilizumab monotherapy. Arthritis Res Ther. 2021;23(1):26.
28. Curtis JR, Chen L, Harrold LR, Narongroeknawin P, Reed G, Solomon DH. Physician preference motivates the use of anti-tumor necrosis factor therapy independent of clinical disease activity. Arthritis Care & Research. 2010;62(1):101-107.
29. Jin Y, Desai RJ, Liu J, Choi NK, Kim SC. Factors associated with initial or subsequent choice of biologic disease-modifying antirheumatic drugs for treatment of rheumatoid arthritis. Arthritis Res Ther. 2017;19(1):159.
30. Novella-Navarro M, Plasencia C, Tornero C, et al. Clinical predictors of multiple failure to biological therapy in patients with rheumatoid arthritis. Arthritis Res Ther. 2020;22(1):284-284.
31. Curtis JR, Jain A, Askling J, et al. A comparison of patient characteristics and outcomes in selected European and U.S. rheumatoid arthritis registries. Seminars in Arthritis and Rheumatism. 2010;40(1):2-14.e11.
32. Bonafede MM, Curtis JR, McMorrow D, Mahajan P, Chen CI. Treatment effectiveness and treatment patterns among rheumatoid arthritis patients after switching from a tumor necrosis factor inhibitor to another medication. Clinicoecon Outcomes Res. 2016;8:707-715.
33. Smolen JS, Kay J, Matteson EL, et al. Insights into the efficacy of golimumab plus methotrexate in patients with active rheumatoid arthritis who discontinued prior anti-tumour necrosis factor therapy: Post-hoc analyses from the go-after study. Annals of the Rheumatic Diseases. 2014;73(10):1811-1818.
34. Genovese MC, Schiff M, Luggen M, et al. Efficacy and safety of the selective co-stimulation modulator abatacept following 2 years of treatment in patients with rheumatoid arthritis and an inadequate response to anti-tumour necrosis factor therapy. Annals of the Rheumatic Diseases. 2008;67(4):547-554.
35. Hyrich KL, Lunt M, Watson KD, Symmons DP, Silman AJ. Outcomes after switching from one anti-tumor necrosis factor alpha agent to a second anti-tumor necrosis factor alpha agent in patients with rheumatoid arthritis: Results from a large UK national cohort study. Arthritis and Rheumatism. 2007;56(1):13-20.
36. Migliore A, Pompilio G, Integlia D, Zhuo J, Alemao E. Cycling of tumor necrosis factor inhibitors versus switching to different mechanism of action therapy in rheumatoid arthritis patients with inadequate response to tumor necrosis factor inhibitors: A Bayesian network meta-analysis. Therapeutic Advances in Musculoskeletal Disease. 2021;13:1759720x211002682.
37. van Mulligen E, Ahmed S, Weel AEAM, Hazes JMW, van der Helm- van Mil AHM, de Jong PHP. Factors that influence biological survival in rheumatoid arthritis: Results of a real-world academic cohort from the Netherlands. Clinical Rheumatology. 2021;40(6):2177-2183.
38. Favalli EG, Biggioggero M, Marchesoni A, Meroni PL. Survival on treatment with second-line biologic therapy: A cohort study comparing cycling and swap strategies. Rheumatology. 2014;53(9):1664-1668.
39. Karpes Matusevich AR, Duan Z, Zhao H, et al. Treatment sequences after discontinuing a tumor necrosis factor inhibitor in patients with rheumatoid arthritis: A comparison of cycling versus swapping strategies. Arthritis Care & Research. 2021;73(10):1461-1469.
40. Poudel D, George MD, Baker JF. The impact of obesity on disease activity and treatment response in rheumatoid arthritis. Current Rheumatology Reports. 2020;22(9):56.
41. Singh S, Facciorusso A, Singh AG, et al. Obesity and response to anti-tumor necrosis factor-α agents in patients with select immune-mediated inflammatory diseases: A systematic review and meta-analysis. PloS One. 2018;13(5):e0195123.
42. Law-Wan J, Sparfel M-A, Derolez S, et al. Predictors of response to tnf inhibitors in rheumatoid arthritis: An individual patient data pooled analysis of randomised controlled trials. RMD Open. 2021;7(3):e001882.
43. Levitsky A, Brismar K, Hafström I, et al. Obesity is a strong predictor of worse clinical outcomes and treatment responses in early rheumatoid arthritis: Results from the Swefot trial. RMD Open. 2017;3(2):e000458.
44. Atzeni F, Antivalle M, Pallavicini FB, et al. Predicting response to anti-tnf treatment in rheumatoid arthritis patients. Autoimmunity Reviews. 2009;8(5):431-437.
45. Avouac J, Gossec L, Dougados M. Diagnostic and predictive value of anti-cyclic citrullinated protein antibodies in rheumatoid arthritis: A systematic literature review. Annals of the Rheumatic Diseases. 2006;65(7):845-851.
46. Mouterde G, Rincheval N, Lukas C, et al. Outcome of patients with early arthritis without rheumatoid factor and ACPA and predictors of rheumatoid arthritis in the ESPOIR cohort. Arthritis Res Ther. 2019;21(1):140.
47. Seegobin SD, Ma MH, Dahanayake C, et al. ACPA-positive and ACPA-negative rheumatoid arthritis differ in their requirements for combination dmards and corticosteroids: Secondary analysis of a randomized controlled trial. Arthritis Res Ther. 2014;16(1):R13.
48. Boer AC, Boonen A, van der Helm van Mil AHM. Is anti-citrullinated protein antibody-positive rheumatoid arthritis still a more severe disease than anti-citrullinated protein antibody-negative rheumatoid arthritis? A longitudinal cohort study in rheumatoid arthritis patients diagnosed from 2000 onward. Arthritis Care & Research. 2018;70(7):987-996.
49. Nordberg LB, Lillegraven S, Aga A-B, et al. Comparing the disease course of patients with seronegative and seropositive rheumatoid arthritis fulfilling the 2010 ACR/EULAR classification criteria in a treat-to-target setting: 2-year data from the arctic trial. RMD Open. 2018;4(2):e000752-e000752.
50. van der Helm-van Mil AHM, Huizinga TWJ. Advances in the genetics of rheumatoid arthritis point to subclassification into distinct disease subsets. Arthritis Res Ther. 2008;10(2):205-205.
51. Padyukov L, Seielstad M, Ong RTH, et al. A genome-wide association study suggests contrasting associations in ACPA-positive versus ACPA-negative rheumatoid arthritis. Annals of the Rheumatic Diseases. 2011;70(2):259-265.
52. Matthijssen XME, Niemantsverdriet E, Huizinga TWJ, van der Helm-van Mil AHM. Enhanced treatment strategies and distinct disease outcomes among autoantibody-positive and -negative rheumatoid arthritis patients over 25 years: A longitudinal cohort study in the Netherlands. PLoS Med. 2020;17(9):e1003296-e1003296.
53. Muilu P, Rantalaiho V, Kautiainen H, Virta LJ, Eriksson JG, Puolakka K. First-year drug therapy of new-onset rheumatoid and undifferentiated arthritis: A nationwide register-based study. BMC Rheumatol. 2020;4:34-34.
54. Lukas C, Mary J, Debandt M, et al. Predictors of good response to conventional synthetic dmards in early seronegative rheumatoid arthritis: Data from the ESPOIR cohort. Arthritis Res Ther. 2019;21(1):243-243.
55. Lv Q, Yin Y, Li X, et al. The status of rheumatoid factor and anti-cyclic citrullinated peptide antibody are not associated with the effect of anti-tnfα agent treatment in patients with rheumatoid arthritis: A meta-analysis. PloS One. 2014;9(2):e89442.
56. Julià A, Erra A, Palacio C, et al. An eight-gene blood expression profile predicts the response to infliximab in rheumatoid arthritis. PloS One. 2009;4(10):e7556-e7556.
57. Tao W, Concepcion AN, Vianen M, et al. Multiomics and machine learning accurately predict clinical response to adalimumab and etanercept therapy in patients with rheumatoid arthritis. Arthritis & Rheumatology (Hoboken, NJ). 2021;73(2):212-222.
58. Farutin V, Prod'homme T, McConnell K, et al. Molecular profiling of rheumatoid arthritis patients reveals an association between innate and adaptive cell populations and response to anti-tumor necrosis factor. Arthritis Res Ther. 2019;21(1):216-216.
59. Mellors T WJ, Ameli A, Jones A, et al. Clinical validation of a blood-based predictive test for stratification of response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis patients. Network and Systems Medicine 2020;3(1):91-104.
60. Tasaki S, Suzuki K, Kassai Y, et al. Multi-omics monitoring of drug response in rheumatoid arthritis in pursuit of molecular remission. Nat Commun. 2018;9(1):2755.
61. Toonen EJ, Gilissen C, Franke B, et al. Validation study of existing gene expression signatures for anti-tnf treatment in patients with rheumatoid arthritis. PloS one. 2012;7(3):e33199.
62. Wright HL, Cox T, Moots RJ, Edwards SW. Neutrophil biomarkers predict response to therapy with tumor necrosis factor inhibitors in rheumatoid arthritis. Journal of Leukocyte Biology. 2017;101(3):785-795.
63. Oswald M, Curran ME, Lamberth SL, et al. Modular analysis of peripheral blood gene expression in rheumatoid arthritis captures reproducible gene expression changes in tumor necrosis factor responders. Arthritis & Rheumatology (Hoboken, NJ). 2015;67(2):344-351.
64. Plant D, Webster A, Nair N, et al. Differential methylation as a biomarker of response to etanercept in patients with rheumatoid arthritis. Arthritis & Rheumatology (Hoboken, NJ). 2016;68(6):1353-1360.
65. Cohen S, Wells AF, Curtis JR, et al. A molecular signature response classifier to predict inadequate response to tumor necrosis factor-α inhibitors: The network-004 prospective observational study. Rheumatology and Therapy. 2021;8(3):1159-1176.
66. Jones A, Rapisardo S, Zhang L, et al. Analytical and clinical validation of an rna sequencing-based assay for quantitative, accurate evaluation of a molecular signature response classifier in rheumatoid arthritis. Expert Review of Molecular Diagnostics. 2021;21(11):1235-1243.
67. Pappas DA, Kremer JM, Reed G, Greenberg JD, Curtis JR. Design characteristics of the corrona certain study: A comparative effectiveness study of biologic agents for rheumatoid arthritis patients. BMC Musculoskelet Disord. 2014;15:113-113.
68. Strand V, Cohen SB, Curtis JR, et al. Clinical utility of therapy selection informed by predicted nonresponse to tumor necrosis factor-a inhibitors: An analysis from the study to accelerate information of molecular signatures (aims) in rheumatoid arthritis. Expert Review of Molecular Diagnostics. 2022;22(1):101-109.
69. Humby F, Durez P, Buch MH, et al. Rituximab versus tocilizumab in anti-tnf inadequate responder patients with rheumatoid arthritis (r4ra): 16-week outcomes of a stratified, biopsy-driven, multicentre, open-label, phase 4 randomised controlled trial. Lancet (London, England). 2021;397(10271):305-317.
70. Anaparti V, Wiens D, O'Neil LJ, et al. Utility of baseline transcriptomic analysis of rheumatoid arthritis synovium as an indicator for long-term clinical outcomes. Front Med (Lausanne). 2022;9:823244-823244.
71. Dennis G Jr, Holweg CT, Kummerfeld SK, et al. Synovial phenotypes in rheumatoid arthritis correlate with response to biologic therapeutics. Arthritis Res Ther. 2014;16(2):R90.
72. Smith SL, Plant D, Lee XH, et al. Previously reported pde3a-slco1c1 genetic variant does not correlate with anti-tnf response in a large UK rheumatoid arthritis cohort. Pharmacogenomics. 2016;17(7):715-720.
73. Sieberts SK, Zhu F, García-García J, et al. Crowdsourced assessment of common genetic contribution to predicting anti-tnf treatment response in rheumatoid arthritis. Nat Commun. 2016;7:12460-12460.
74. Ho CH, Silva AA, Giles JT, et al. Validation of a bioassay for predicting response to tumor necrosis factor inhibitors in rheumatoid arthritis. Arthritis & Rheumatology (Hoboken, NJ). 2021;73(6):1086-1087.
75. Kedra J, Radstake T, Pandit A, et al. Current status of use of big data and artificial intelligence in rmds: A systematic literature review informing EULAR recommendations. RMD Open. 2019;5(2):e001004.
76. Gossec L, Kedra J, Servy H, et al. EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases. Annals of the Rheumatic Diseases. 2020;79(1):69.
77. Boegel S, Castle JC, Schwarting A. Current status of use of high throughput nucleotide sequencing in rheumatology. RMD Open. 2021;7(1):e001324.
78. Peng J, Jury EC, Dönnes P, Ciurtin C. Machine learning techniques for personalised medicine approaches in immune-mediated chronic inflammatory diseases: Applications and challenges. Front Pharmacol. 2021;12:720694-720694.
79. Pappas DA, Brittle C, Mossell JE 3rd, Withers JB, Lim-Harashima J, Kremer JM. Perceived clinical utility of a test for predicting inadequate response to tnf inhibitor therapies in rheumatoid arthritis: Results from a decision impact study. Rheumatology International. 2021;41(3):585-593.
80. Santos-Moreno P, Sánchez G, Castro C. Rheumatoid factor as predictor of response to treatment with anti-tnf alpha drugs in patients with rheumatoid arthritis: Results of a cohort study. Medicine. 2019;98(5):e14181-e14181.
81. Michelsen B, Berget KT, Loge JH, Kavanaugh A, Haugeberg G. Sex difference in disease burden of inflammatory arthritis patients treated with tumor necrosis factor inhibitors as part of standard care. PloS One. 2022;17(5):e0266816.
82. Matsui T, Kuga Y, Kaneko A, et al. Disease activity score 28 (das28) using c-reactive protein underestimates disease activity and overestimates EULAR response criteria compared with das28 using erythrocyte sedimentation rate in a large observational cohort of rheumatoid arthritis patients in japan. Annals of the Rheumatic Diseases. 2007;66(9):1221-1226.
83. Lee S, Kang S, Eun Y, et al. Machine learning-based prediction model for responses of bDMARDs in patients with rheumatoid arthritis and ankylosing spondylitis. Arthritis Res Ther. 2021;23(1):254-254.
84. Fautrel B, Alten R, Kirkham B, et al. Call for action: How to improve use of patient-reported outcomes to guide clinical decision making in rheumatoid arthritis. Rheumatology International. 2018;38(6):935-947.
85. Barber CEH, Zell J, Yazdany J, et al. American College of Rheumatology recommended patient-reported functional status assessment measures in rheumatoid arthritis. Arthritis Care & Research. 2019;71(12):1531-1539.
86. Koo BS, Eun SK, Shin K, et al. Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics. Arthritis Research & Therapy. 2021;23.
87. Riley RD, Snell KI, Ensor J, et al. Minimum sample size for developing a multivariable prediction model: Part ii - binary and time-to-event outcomes. Stat Med. 2019;38(7):1276-1296.
88. Thomas SS, Borazan N, Barroso N, et al. Comparative immunogenicity of tnf inhibitors: Impact on clinical efficacy and tolerability in the management of autoimmune diseases. A systematic review and meta-analysis. BioDrugs: Clinical Immunotherapeutics, Biopharmaceuticals and Gene Therapy. 2015;29(4):241-258.

• Other (LCD request by Laboratory)

• Rheumatoid Arthritis