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

This is a limited coverage policy for molecular biomarker tests to guide targeted therapy selection in Rheumatoid Arthritis (RA).

Coverage criteria:

1. The patient is an adult with a confirmed diagnosis of moderately to severely active RA.

2. The patient has a history of failure, contraindication, or intolerance to at least one first-line therapy for the treatment of RA (i.e., conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs)) despite adequate dosing.

3. The patient has not initiated a biologic or targeted synthetic therapy (b/tDMARD) for RA (i.e., Tumor Necrosis Factor-?? inhibitor [TNFi], Janus Kinase [JAK] inhibitor, etc.) OR has initiated b/tDMARD therapy and is being considered for an alternate class of targeted therapies as a result of failure, contraindication, or intolerance to the initial targeted therapy despite adequate dosing.

4. The test predicts response and/or non-response to at least one class of targeted or biologic therapies for RA according to multiple validated response/remission criteria (a) with an accuracy that exceeds that which can be obtained from the combination of existing clinical and other data AND (b) with demonstrated reproducibility across clinical study cohorts.

5. Testing using molecular biomarkers has not been previously performed for predictive therapy selection in RA.

6. Testing is performed according to the intended use of the test in the intended patient population for which the test was developed and validated.

7. The test demonstrates analytical validity (AV), clinical validity (CV) and clinical utility (CU), establishing a clear and significant biological/molecular basis for stratifying patients and subsequently selecting (either positively or negatively) a clinical management in a clearly defined population.

8. Clinical validity of any analytes (or expression profiles) measured must be established through a study published in the peer-reviewed published literature for the intended use of the test in the intended population.

9. If the test relies on an algorithm, the algorithm must be validated in a cohort that is not a development cohort for the algorithm.

10. The lab providing the test is responsible for clearly indicating to treating physicians the population and indication(s) for test use.

11. The test successfully completes a Molecular Diagnostic Services Program (MolDX^®) Technical Assessment that ensures that AV, CV, and CU criteria set in this policy are met to establish the test as Reasonable and Necessary.

12. If applicable, performance characteristics are equivalent or superior to the average performance of other similar tests (for the same intended use) evaluated by this contractor upon successful completion of a technical assessment.

Since the clinical utility of predictive testing is largely dependent upon consensus-based management recommendations, this coverage decision is subject to change pending changes in the literature and in consensus guidelines.

Finally, new tests that become available with significantly improved performance may render older tests no longer compliant with this policy.

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)).^17,18 Other 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 b/tDMARDs 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 with multiple trial-and-error sequential approaches to treatment.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 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). The one such test that is most widely published is a molecular signature response classifier (MSRC) from Scipher Medicine (Waltham, MA) 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 a TNFi response rate of 10.3% (7/68) by ACR50.⁵⁹ 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 Importantly, both of these studies were observational and test results were not actually used to inform treatment selection. Additionally, treatment selection was at the physician's discretion and may have been influenced by multiple clinical and non-clinical factors, as outlined above.

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/tDMARD treatment decision was informed by MSRC testing.⁶⁸ Patient eligibility did not consider baseline disease activity, prior biological exposure, or csDMARD use. 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; by CDAI they were 56.2% and 15.4%, respectively.⁶⁸ ACR50 response to TNFis in patients lacking the NR signature was only 45.8%.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, once again highlighting the multiple variables associated with physician prescribing practices. A second interim analysis of AIMS that included a larger number of patients (N=274) with moderate or severe RA similarly showed that absolute changes in CDAI scores from baseline were improved when treatment was informed by the MSRC test.⁶⁹ Finally, a comparative cohort study compared a MSRC-tested arm from AIMS with an external control arm from a United States electronic health records database.⁷⁰ This study validated the reported test performance characteristics of the MSCR (i.e. PPV 88%) but again noted that physicians prescribed test-aligned therapies only 70% of the time. Despite this incomplete adherence to test results, patients in the MSRC arm were nearly 3 times more likely to achieve remission than those in the standard-of-care control arm. ⁷⁰ Importantly, the authors reported that after cohort matching, 57% of patients in the MSRC-tested arm (N=489 with clinical outcomes data at 6 months) had not been included in the 2 prior interim analyses, thus providing data independent of the two prior AIMS-based cohort studies.⁷⁰

There are other predictive biomarker tests that are ‘biopsy-driven,’ evaluating the transcriptomic signature of the synovium in RA patients as an indicator for therapy response and clinical outcomes.^71,72 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.⁷¹ 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.⁷¹ Another longitudinal study of the synovial transcriptome reported differentially expressed genes in DMARD-naïve early-RA patients versus advanced RA patients.⁷² 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.⁷³ 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.⁷⁴ 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.⁷⁵ 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.⁷⁵ 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.⁷⁵

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.^60,76 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.^58,77-79 These issues lend themselves to the risks of ‘quantitative fallacy,’ bias, overfitting of predictive models, and the inability to generalize results.^77,80 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.⁷⁸

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.⁸¹ However, though the panelists were aware of early and emerging data, they were not aware that any such tests had been rigorously validated for routine clinical use at the time of the CAC. 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, during the Comment period, we received letters from our SMEs in support of the use of predictive biomarker tests for a limited RA population.

TNFi therapies are often the first biologic therapy used to treat RA patients who have not responded to csDMARDs for several reasons, some of which are not evidence based. However, many patients do not adequately respond to TNFis. Therefore, a test that can accurately predict response to such therapies 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. RA is a heterogeneous disease in terms of pathogenesis as well as treatment response. Additionally, baseline demographic and clinical patient characteristics such as obesity, female sex, and the presence of RF, have each been associated with a lack of response to TNFi therapy.^42,82,83 In published studies, these factors may have impacted observed treatment responses as well as provider treatment decisions independently of any test result. Despite this, the MSRC has recently demonstrated through a multivariable analysis that it is a better predictor of ACR50 nonresponse to TNFi therapy than any single clinical feature or combination of clinical features, confirming that the genomic component of the test contributes to the test performance above the contributions from the available clinical, demographic and laboratory parameters that are included as part of the test.⁸⁴

Our review of the current literature demonstrates that there are multiple limitations not only of the current biomarker tests for RA therapy selection, but also of the disease response assessments against which they are measured. First, 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.⁸⁵ 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 Second, 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.⁸⁶ 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.^66,68 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.^16,87,88

The current literature illuminates many limitations of the evaluated evidence supporting the use of these RA biomarker tests. One major issue is that they 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.⁸⁹ 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.

There are also limitations in the various studies that may have significantly influenced the treatments provided and conclusions drawn by the authors, including but not limited to the following: patients with conflicting information in the AIMS database relative to primary source documents were excluded without further investigation; patients in the CERTAIN trial were allowed to contribute multiple sets of observations if they initiated different biologics over the study period and if they did so, a new ‘baseline’ visit was established; patients who switched to a new biologic, even if for reasons of safety rather than efficacy were considered as non-responders; in the CERTAIN trial, the reasons for discontinuation were not systematically collected for all of the years of the study period, and discontinuation may have occurred for a variety of reasons unrelated to efficacy; most authors of the MSRC papers were Scipher Medicine employees or had heavy financial ties to the company.

The MSRC only provides information regarding response to TNFi therapies and does not provide information regarding the various alternate therapies that are available. Moreover, the test will only identify about 55% of the TNFi non-responders.⁵⁹ The remaining half of true NR patients will still likely be treated with TNFis and most of them will not achieve clinical disease response or remission. Additionally, 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).⁹¹ Therefore, the utility of a test like the MSRC is limited to only a subset of the RA population. It is thus 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.^80,90 For many of these reasons, international, national, and society guidelines have not yet endorsed predictive (for response to therapy) biomarker testing in RA.

Despite the many limitations of predictive biomarker tests, a review of the evidence, as imperfect as it may be, supports the limited use of these services given their demonstrated validity and utility. When a NR signature is obtained by the MSRC, nearly 90% of those patients will prove to not clinically respond to TNFi therapies using multiple validated disease response criteria including the ACR50 and CDAI.⁵⁹ In these patients, a non-TNFi is likely the best therapeutic option. While the high PPV of this test is also a function of the high (~60-70%) prevalence of true TNFi- non-responders in this population, the incremental (~20%) improvement over the already known high prevalence of NR at baseline (uninformed by the test) is still considered an improved outcome by this contractor despite its marginal benefit to these patients. Further, although there is no guarantee that the use of a non-TNFi in a patient with a NR-signature will result in improved disease outcomes, as only about 35% of these patients respond to alternate therapies by ACR50, similar to the response rate to TNFis reported in the overall population at baseline (though 56.2% of these patients respond to the alt-MOA therapies when the CDAI measure of disease activity is used),⁶⁸ this change in management would ultimately serve to avoid time on an unnecessary therapy and shorten the time to an appropriate therapy. Despite this, additional challenges remain as studies have shown that (a) physicians still prescribe TNFis in many patients with NR signatures, (b) many patients will still often cycle to a second TNFi before switching to a non-TNFi (despite ACR guidelines) and (c) patients who begin or switch to a non-TNFi tend to be older with more comorbidities. Therefore, it is not immediately clear that the use of this test will, in fact, result in a significant shift in physician prescribing even in the appropriate TNFi-NR populations.

There is an apparent need to identify appropriate (or inappropriate) targeted therapies for RA patients. While the current data on the use of these services is very limited, a review of the published evidence to date demonstrates a marginal but pragmatic value to the use of some RA therapy selection tests in a limited setting. However, it is imperative to underscore that the utility of the reviewed tests remains limited to only half of the true TNFi-NR population and that even in this group, there is no guarantee that the alternate (non-TNFi) therapies prescribed as a result of the test will result in improved disease activity or remission. Therefore, it is likely that future tests will add substantially more benefit than the current services offered and reviewed by this contractor.

Finally, 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 testing to avoid such preferences that is not grounded in evidence.

This contractor will continue to monitor the evidence and may modify coverage based on new information in the pertinent literature and society recommendations.

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1. Lopez-Pedrera C, Barbarroja N, Patiño-Trives AM, et al. Effects of biological therapies on molecular features of rheumatoid arthritis. Int J Mol Sci. 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 June 29, 2023.
3. Smolen JS, Aletaha D, McInnes IB. Rheumatoid arthritis. Lancet. 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. Ann Rheum Dis. 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. Ann Rheum Dis. 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 Res(Hoboken). 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 Rheum. 2011;63(3):573-586.
9. Bykerk VP, Massarotti EM. The new ACR/EULAR remission criteria: rationale for developing new criteria for remission. Rheumatology(Oxford). 2012;51(suppl_6):vi16-vi20.
10. Kay J, Upchurch KS. ACR/EULAR 2010 rheumatoid arthritis classification criteria. Rheumatology(Oxford). 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 Rheum. 1995;38(6):727-735.
12. Hobbs KF, Cohen MD. Rheumatoid arthritis disease measurement: a new old idea. Rheumatology(Oxford). 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. Ann Rheum Dis. 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 Res(Hoboken). 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. Ann Rheum Dis. 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. Ann Rheum Dis. 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. Ann Rheum Dis. 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. Rheum Int. 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 Res(Hoboken). 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 Res(Hoboken). 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 andU.S. rheumatoid arthritis registries. Semin Arthritis Rheum. 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. Ann Rheum Dis. 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. Ann Rheum Dis. 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 Rheum. 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. Ther Adv Musculoskelet Dis. 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. Clin Rheumatol. 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(Oxford). 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 Res(Hoboken). 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. Curr Rheumatol Rep. 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. Autoimmun Rev. 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. Ann Rheum Dis. 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 Res(Hoboken). 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. Ann Rheum Dis. 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-TNFa 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 Rheumatol. 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, Wang M, 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. J Leukoc Biol. 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 Rheumatol. 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 Rheumatol. 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. Rheumatol Ther. 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 Rev Mol Diagn. 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-? inhibitors: an analysis from the study to accelerate information of molecular signatures (AIMS) in rheumatoid arthritis. Expert Rev Mol Diagn. 2022;22(1):101-109.
69. Strand V, Zhang L, Arnaud A, Connolly-Strong E, Asgarian S, Withers JB. Improvement in clinical disease activity index when treatment selection is informed by the tumor necrosis factor-? inhibitor molecular signature response classifier: Analysis from the a study to accelerate information of molecular signatures in rheumatoid arthritis. Expert Opin Biol Ther. 2022;22(6):801-807.
70. Curtis JR, Strand V, Golombek S, et al. Patient outcomes improve when a molecular signature test guides treatment decision-making in rheumatoid arthritis. Expert Rev Mol Diagn. 2022:1-10.
71. 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. 2021;397(10271):305-317.
72. 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.
73. 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.
74. Smith SL, Plant D, Lee XH, et al. Previously reported PDE3A-SLCO1C1 genetic variant does not correlate with anti-TNF response in a large UKrheumatoid arthritis cohort. Pharmacogenomics. 2016;17(7):715-720.
75. 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.
76. Ho CH, Silva AA, Giles JT, et al. Validation of a bioassay for predicting response to tumor necrosis factor inhibitors in rheumatoid arthritis. Arthritis Rheumatol. 2021;73(6):1086-1087.
77. Kedra J, Radstake T, Pandit A, et al. Current status of use of big data and artificial intelligence inRMDS: a systematic literature review informing eular recommendations. RMD Open. 2019;5(2):e001004.
78. Gossec L, Kedra J, Servy H, et al. EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases. Ann Rheum Dis. 2020;79(1):69.
79. Boegel S, Castle JC, Schwarting A. Current status of use of high throughput nucleotide sequencing in rheumatology. RMD Open. 2021;7(1):e001324.
80. 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.
81. 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. Rheumatol Int. 2021;41(3):585-593.
82. 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.
83. 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.
84. Cohen S, Curtis JR, Mellors T, et al. Commentary on cohen et al.: role of clinical factors in precision medicine test to predict nonresponse to tnfi therapies in rheumatoid arthritis. Rheumatol Ther. 2023;10(1):1-6.
85. 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. Ann Rheum Dis. 2007;66(9):1221-1226.
86. 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.
87. 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. Rheumatol Int. 2018;38(6):935-947.
88. Barber CEH, Zell J, Yazdany J, et al. 2019 American College of Rheumatology recommended patient-reported functional status assessment measures in rheumatoid arthritis. Arthritis Care Res(Hoboken). 2019;71(12):1531-1539.
89. 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 Res Ther. 2021;23(1):178.
90. 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.
91. 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. 2015;29(4):241-258

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