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-naive 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 2nd 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.