Clinical Utility and Cost Savings in Predicting Inadequate Response to Anti-TNF Therapies in Rheumatoid Arthritis

Real-World Treatment and Care Patterns in Patients With Rheumatoid Arthritis Initiating First-Line Tumor Necrosis Factor Inhibitor Therapy in the United States

OBJECTIVE

Treatment guidelines for rheumatoid arthritis (RA) recommend targeting low disease activity or remission and switching therapies for patients not reaching those targets. We evaluated real-world use of disease activity measures, treatment discontinuation, and switching patterns among patients with RA initiating a first-line tumor necrosis factor inhibitor (TNFi).

METHODS

Data from adult patients with RA initiating a first-line TNFi were collected from the American Rheumatology Network (January 2014-August 2021). The proportion of patients with recorded disease activity scores (Clinical Disease Activity Index [CDAI] or Routine Assessment of Patient Index Data 3 [RAPID3]) at TNFi initiation was assessed. Among patients with moderate or severe RA at TNFi initiation, reasons for discontinuation and subsequent advanced therapy were evaluated.

RESULTS

Among TNFi initiators (n = 15,182), 44.8% recorded a CDAI/RAPID3 score at treatment initiation; of those who did not, 47.0% had recorded a tender and/or swollen joint count or pain score. Among patients with moderate or severe RA (n = 1,651), 52% discontinued their initial TNFi during follow-up, of which 15%, 46%, 28%, and 12% initiated the same TNFi, another TNFi, a non-TNFi biologic, or a Janus kinase inhibitor, respectively. The proportion of patients restarting the same TNFi or initiating another TNFi varied according to TNFi discontinuation reason.

CONCLUSION

In clinical practice, over half of patients with RA initiating a first-line TNFi did not have baseline disease activity assessments. Many patients cycled through TNFi despite citing lack of efficacy as the most common reason for discontinuation. Consistent, objective monitoring of treatment response and timely switch to effective therapy is needed in patients with RA.

Real-World Use and Effectiveness Outcomes in Patients with Rheumatoid Arthritis Treated with Upadacitinib: An Analysis from the CorEvitas Registry

INTRODUCTION

Data assessing longer-term real-world effectiveness and treatment patterns with upadacitinib (UPA), a Janus kinase inhibitor, in rheumatoid arthritis (RA) are lacking. We assessed improvement in clinical and patient-reported outcomes and treatment patterns for up to 12 months among adult patients with RA initiating UPA.

METHODS

Data were collected from the CorEvitas® RA Registry (08/2019-04/2022). Eligible patients had moderate to severe RA (Clinical Disease Activity Index [CDAI] > 10) and follow-up visits at 6 or 12 months after UPA initiation. Outcomes were mean change from baseline, percentage achieving minimal clinically important differences (MCID) in clinical and patient-reported outcomes, and disease activity at follow-up. We evaluated clinical outcomes and therapy changes among patients with tumor necrosis factor inhibitor (TNFi) experience and among those receiving UPA as first-line therapy, as well as those receiving UPA as monotherapy versus as part of combination therapy. We further evaluated whether outcomes were similar among those that remained on therapy.

RESULTS

Patients treated with UPA (6-month cohort, N = 469; 12-month cohort, N = 263) had statistically significant improvements (p < 0.001) in mean CDAI, tender/swollen joint counts, pain, and fatigue at follow-up. At 12 months, 46.0% achieved MCID in CDAI and 40.0% achieved low disease activity/remission. Overall, 43.0% discontinued UPA at 12 months; of those receiving combination treatment (N = 90) with conventional therapies and UPA, 42.2% (N = 38) discontinued conventional therapy. Findings were similar in the 6-month cohort and among subgroups. Changes from baseline and proportions of patients achieving MCID or clinical outcomes tended to be numerically lower among patients with TNFi experience and numerically higher among those receiving UPA as first-line therapy.

CONCLUSIONS

UPA initiation was associated with improvements in clinical and patient-reported outcomes, with meaningful clinical improvements regardless of prior TNFi experience, line of therapy, or concomitant use of conventional therapies. Further research is needed to better understand sustained response of UPA over longer treatment periods.

One-Year Medication Adherence and Persistence in Rheumatoid Arthritis in Clinical Practice: A Retrospective Analysis of Upadacitinib, Adalimumab, Baricitinib, and Tofacitinib

INTRODUCTION

This study evaluated 12 months adherence and persistence among Janus kinase inhibitors (upadacitinib, baricitinib, tofacitinib) and adalimumab, a tumor necrosis factor inhibitor (TNFi), in patients with rheumatoid arthritis (RA).

METHODS

This retrospective analysis used administrative claims data from the Merative™ MarketScan® Research Databases (2018-2022). Eligible adults had ≥ 1 RA diagnosis before the index date, ≥ 1 pharmacy claim for index medication, and ≥ 12 months of continuous insurance enrollment pre- and post-index. Adherence to treatment [defined as proportion of days covered (PDC) ≥ 80%], risk of treatment discontinuation, and mean time to discontinuation were assessed during the 12 months follow-up. Adjusted odds ratios (aOR), adjusted hazard ratios (aHR), and 95% confidence intervals (CI) were reported.

RESULTS

In total, 6317 patients were included (683 upadacitinib, 3732 adalimumab, 132 baricitinib, 1770 tofacitinib). Compared with upadacitinib, patients initiating adalimumab [aOR (95% CI): 0.82 (0.69, 0.96)], baricitinib [0.46 (0.31, 0.68)], and tofacitinib [0.74 (0.62, 0.88)] were significantly less likely to achieve PDC ≥ 80%. Risk of treatment discontinuation was significantly higher in patients treated with adalimumab [aHR (95% CI): 1.14 (1.01, 1.29)], baricitinib [1.48 (1.16, 1.90)], and tofacitinib [1.22 (1.07, 1.38)] compared with upadacitinib. Mean time to discontinuation was 256 (upadacitinib), 249 (adalimumab), 221 (baricitinib), and 239 (tofacitinib) days. Similar results were observed in patients with prior TNFi use.

CONCLUSIONS

Patients with RA, regardless of recent TNFi experience, initiating upadacitinib were significantly more likely to be adherent and less likely to discontinue therapy compared to adalimumab, baricitinib, and tofacitinib in the first 12 months of treatment.

Real-World Sarilumab Use and Rule Testing to Predict Treatment Response in Patients with Rheumatoid Arthritis: Findings from the RISE Registry

INTRODUCTION

Clinical trial findings may not be generalizable to routine practice. This study evaluated sarilumab effectiveness in patients with rheumatoid arthritis (RA) and tested the real-world applicability of a response prediction rule, derived from trial data using machine learning (based on C-reactive protein [CRP] > 12.3 mg/l and seropositivity [anticyclic citrullinated peptide antibodies, ACPA +]).

METHODS

Sarilumab initiators from the ACR-RISE Registry, with ≥ 1 prescription on/after its FDA approval (2017-2020), were divided into three cohorts based on progressively restrictive criteria: Cohort A (had active disease), Cohort B (met eligibility criteria of a phase 3 trial in RA patients with inadequate response/intolerance to tumor necrosis factor inhibitors [TNFi]), and Cohort C (characteristics matched to the phase 3 trial baseline). Mean changes in Clinical Disease Activity Index (CDAI) and Routine Assessment of Patient Index Data 3 (RAPID3) were evaluated at 6 and 12 months. In a separate cohort, predictive rule was tested based on CRP levels and seropositive status (ACPA and/or rheumatoid factor); patients were categorized into rule-positive (seropositive with CRP > 12.3 mg/l) and rule-negative groups to compare the odds of achieving CDAI low disease activity (LDA)/remission and minimal clinically important difference (MCID) over 24 weeks.

RESULTS

Among sarilumab initiators (N = 2949), treatment effectiveness was noted across cohorts, with greater improvement noted for Cohort C at 6 and 12 months. Among the predictive rule cohort (N = 205), rule-positive (vs. rule-negative) patients were more likely to reach LDA (odds ratio: 1.5 [0.7, 3.2]) and MCID (1.1 [0.5, 2.4]). Sensitivity analyses (CRP > 5 mg/l) showed better response to sarilumab in rule-positive patients.

CONCLUSIONS

In real-world setting, sarilumab demonstrated treatment effectiveness, with greater improvements in the most selective population, mirroring phase 3 TNFi-refractory and rule-positive RA patients. Seropositivity appeared a stronger driver for treatment response than CRP, although optimization of the rule in routine practice requires further data.

Tailored therapeutic decision of rheumatoid arthritis using proteomic strategies: how to start and when to stop?

Unpredictable treatment responses have been an obstacle for the successful management of rheumatoid arthritis. Although numerous serum proteins have been proposed, there is a lack of integrative survey to compare their relevance in predicting treatment outcomes in rheumatoid arthritis. Also, little is known about their applications in various treatment stages, such as dose modification, drug switching or withdrawal. Here we present an in-depth exploration of the potential usefulness of serum proteins in clinical decision-making and unveil the spectrum of immunopathology underlying responders to different drugs. Patients with robust autoimmunity and inflammation are more responsive to biological treatments and prone to relapse during treatment de-escalation. Moreover, the concentration changes of serum proteins at the beginning of the treatments possibly assist early recognition of treatment responders. With a better understanding of the relationship between the serum proteome and treatment responses, personalized medicine in rheumatoid arthritis will be more achievable in the near future.

Longitudinal home self-collection of capillary blood using homeRNA correlates interferon and innate viral defense pathways with SARS-CoV-2 viral clearance

Blood transcriptional profiling is a powerful tool to evaluate immune responses to infection; however, blood collection via traditional phlebotomy remains a barrier to precise characterization of the immune response in dynamic infections (e.g., respiratory viruses). Here we present an at-home self-collection methodology, homeRNA, to study the host transcriptional response during acute SARS-CoV-2 infections. This method uniquely enables high frequency measurement of the host immune kinetics in non-hospitalized adults during the acute and most dynamic stage of their infection. COVID-19+ and healthy participants self-collected blood every other day for two weeks with daily nasal swabs and symptom surveys to track viral load kinetics and symptom burden, respectively. While healthy uninfected participants showed remarkably stable immune kinetics with no significant dynamic genes, COVID-19+ participants, on the contrary, depicted a robust response with over 418 dynamic genes associated with interferon and innate viral defense pathways. When stratified by vaccination status, we detected distinct response signatures between unvaccinated and breakthrough (vaccinated) infection subgroups; unvaccinated individuals portrayed a response repertoire characterized by higher innate antiviral responses, interferon signaling, and cytotoxic lymphocyte responses while breakthrough infections portrayed lower levels of interferon signaling and enhanced early cell-mediated response. Leveraging cross-platform longitudinal sampling (nasal swabs and blood), we observed that IFI27, a key viral response gene, tracked closely with SARS-CoV-2 viral clearance in individual participants. Taken together, these results demonstrate that at-home sampling can capture key host antiviral responses and facilitate frequent longitudinal sampling to detect transient host immune kinetics during dynamic immune states.

Association of tumor necrosis factor-alpha promoter region gene polymorphism at positions -308G/A, -857C/T, and -863C/A with etanercept response in Iraqi rheumatoid arthritis patients

Objectives

This study aims to evaluate the association between polymorphisms in the promoter region of the tumor necrosis factor-alpha (TNF-α) gene at locations -308G/A, -857C/T, and -863C/A with the tendency of being non-responder to etanercept.

Patients and methods

Between October 2020 and August 2021, a total of 80 patients (10 males, 70 females; mean age: 50 years; range, 30 to 72 years) with rheumatoid arthritis (RA) receiving etanercept for at least six months were included. The patients were divided into two groups responders and non-responders, based on their response after six months of continuous treatment. Following polymerase chain reaction amplification of the extracted deoxyribonucleic acid, sequencing by Sanger method was performed to identify the polymorphism at the TNF-α promoter region.

Results

In the responder group, the GG genotype of (-308G/A) and the AA genotype of (-863C/A) were both significantly present. The CC genotype of (-863C/A) was significantly present in the non-responders group. The CC of (-863C/A) SNP was the only genotype that appeared to increase the likelihood of being resistant to etanercept. The GG genotype of (-308G/A) was negatively correlated with the likelihood of being a non-responder. The (-857CC) and (-863CC) genotypes were significantly more prevalent in the non-responders group.

Conclusion

The presence of the (-863CC) genotype, alone or in combination with (-857CC), is linked to an increased likelihood of becoming a non-responder to etanercept. The GG genotype of -308G/A and the AA genotype of -863C/A significantly increase the likelihood of becoming responder to etanercept.

IL-10 Induced by mTNF Crosslinking-Mediated Reverse Signaling in a Whole Blood Assay Is Predictive of Response to TNFi Therapy in Rheumatoid Arthritis

(1) Background: To date, the response of patients with rheumatoid arthritis (RA) to the various biologic DMARD available cannot be predicted due to a lack of reliable biomarkers. Based on our preliminary work on tmTNF reverse signaling, we developed a whole-blood assay measuring tmTNF crosslinking-induced IL-10 production to predict the response to TNF inhibitor (TNFi) therapy. (2) Methods: This prospective study included patients with active RA. Depending on the clinical judgment of the attending rheumatologist, either therapy with a TNF or JAK inhibitor was initiated. Clinical parameters and blood samples were obtained at baseline and after 8 weeks of therapy. The blood samples were collected using a newly developed whole-blood assay based on the principle of tmTNF reverse signalling. Subsequently, IL-10 was measured via enzyme-linked immunosorbent assay (ELISA) technique. (3) Results: 63 patients with RA were enrolled. In fifteen patients, TNFi therapy was initiated, while eight patients started a JAKi treatment. The cross-sectional analysis of all patients showed a positive correlation between tmTNF crosslinking-induced IL-10 and parameters of disease activity (CRP [r = 0.4091, p = 0.0009], DAS28 [r = 0.3303, p = 0.0082]) at baseline. In the TNFi treatment study, IL-10 was found to be significantly higher in EULAR responders than in non-responders (p = 0.0033). After initiation of JAKi treatment, in contrast, IL-10 induction was not linked to response. Longitudinal analysis of the TNFi-treated patients revealed IL-10 to decrease in responders (p = 0.04), but not in non-responders after 8 weeks of therapy. Of importance, the IL-10 production at baseline correlated inversely with TNFi response determined by ΔDAS28 in patients with TNFi treatment (r = −0.5299, p = 0.0422) while no such link was observed under JAKi therapy (p = 0.22). Receiver operation characteristics (ROC) analysis demonstrated a high performance of tmTNF/crosslinking-induced IL-10 in predicting a TNFi therapy response according to the EULAR criteria (AUC = 0.9286, 95% Confidence interval 0.7825–1.000, p = 0.0055). (4) Conclusions: In this pilot investigation, we demonstrated the feasibility of a whole-blood assay measuring tmTNF-induced IL-10 to predict clinical response to TNF inhibitor treatment. This approach might support rheumatologists in their decision for an individually tailored RA therapy.

Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma

Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.

Longitudinal analysis of blood DNA methylation identifies mechanisms of response to tumor necrosis factor inhibitor therapy in rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a chronic, immune-mediated inflammatory disease of the joints that has been associated with variation in the peripheral blood methylome. In this study, we aim to identify epigenetic variation that is associated with the response to tumor necrosis factor inhibitor (TNFi) therapy.

Methods

Peripheral blood genome-wide DNA methylation profiles were analyzed in a discovery cohort of 62 RA patients at baseline and at week 12 of TNFi therapy. DNA methylation of individual CpG sites and enrichment of biological pathways were evaluated for their association with drug response. Using a novel cell deconvolution approach, altered DNA methylation associated with TNFi response was also tested in the six main immune cell types in blood. Validation of the results was performed in an independent longitudinal cohort of 60 RA patients.

Findings

Treatment with TNFi was associated with significant longitudinal peripheral blood methylation changes in biological pathways related to RA (FDR<0.05). 139 biological functions were modified by therapy, with methylation levels changing systematically towards a signature similar to that of healthy controls. Differences in the methylation profile of T cell activation and differentiation, GTPase-mediated signaling, and actin filament organization pathways were associated with the clinical response to therapy. Cell type deconvolution analysis identified CpG sites in CD4+T, NK, neutrophils and monocytes that were significantly associated with the response to TNFi.

Interpretation

Our results show that treatment with TNFi restores homeostatic blood methylation in RA. The clinical response to TNFi is associated to methylation variation in specific biological pathways, and it involves cells from both the innate and adaptive immune systems.

Funding

The Instituto de Salud Carlos III.

Clinical utility of therapy selection informed by predicted nonresponse to tumor necrosis factor-ɑ inhibitors: an analysis from the Study to Accelerate Information of Molecular Signatures (AIMS) in rheumatoid arthritis

BACKGROUND

The molecular signature response classifier (MSRC) is a blood-based precision medicine test that predicts nonresponders to tumor necrosis factor-ɑ inhibitors (TNFi) in rheumatoid arthritis (RA) so that patients with a molecular signature of non-response to TNFi can be directed to a treatment with an alternative mechanism of action.

RESEARCH DESIGN AND METHODS

This study evaluated decision choice and treatment outcomes resulting from MSRC-informed treatment selection within a real-world cohort.

RESULTS

Therapy selection by providers was informed by MSRC results for 73.5% (277/377) of patients. When MSRC results were not incorporated into decision-making, 62.0% (62/100) of providers reported deviating from test recommendations due to insurance-related restrictions. The 24-week ACR50 responses in patients prescribed a therapy in alignment with MSRC results were 39.6%. Patients with a molecular signature of non-response had significantly improved responses to non-TNFi therapies compared with TNFi therapies (ACR50 34.8% vs 10.3%, p-value = 0.05). This indicates that predicted non-responders to TNFi therapies are not nonresponders to other classes of RA targeted therapy. Significant changes were also observed for CDAI, ACR20, ACR70, and for responses at 12 weeks.

CONCLUSIONS

Adoption of the MSRC into patient care could fundamentally shift treatment paradigms in RA, resulting in substantial improvements in real-world treatment outcomes.

Could potentially calprotectin be a promising biomarker to oracle biologic therapy response in rheumatoid arthritis?

Background

The advent of novel biologic agents for the treatment of rheumatoid arthritis (RA) has proven to be highly productive. Nonetheless, high cost, side effects, and unresponsiveness to these agents dictates the assignment of biomarkers that can foretell treatment response. Currently, calprotectin (a member of the S100 protein family) is amongst the enormously studied candidates in this perspective. Yet, conflicting results have been published. The main purpose of this study was to explore the role of serum concentration of calprotectin to predict the response to biological therapy in RA patients, so as to customize RA treatment.

Results

Baseline serum calprotectin levels were significantly higher in RA patients compared to the control subjects (P value < 0.001). After receiving biologic therapy, a remarkable reduction (P < 0.001) in serum calprotectin was noted in RA cohort. Moreover, no correlation was found between the 28 joint count disease activity score (DAS28) and serum calprotectin levels neither before or after biologics. Intriguingly, no statistically significant association was detected between circulating calprotectin level and response to biological therapy.

Conclusion

Serum calprotectin concentrations could not be used as a biomarker to forecast clinical response to biological therapy in RA patients. However, further studies involving larger cohort of RA patients should be carried out to deliver more insight in this regard.

Analytical and clinical validation of an RNA sequencing-based assay for quantitative, accurate evaluation of a molecular signature response classifier in rheumatoid arthritis

OBJECTIVES

This study reports analytical and clinical validation of a molecular signature response classifier (MSRC) that identifies rheumatoid arthritis (RA) patients who are non-responders to tumor necrosis factor-ɑ inhibitors (TNFi).

METHODS

The MSRC integrates patient-specific data from 19 gene expression features, anti-cyclic citrullinated protein serostatus, sex, body mass index, and patient global assessment into a single score.

RESULTS

The MSRC results stratified samples (N = 174) according to non-response prediction with a positive predictive value of 87.7% (95% CI: 78-94%), sensitivity of 60.2% (95% CI: 50-69%), and specificity of 77.3% (95% CI: 65-87%). The 25-point scale was subdivided into three thresholds: signal not detected (<10.6), high (≥10.6), and very high (≥18.5). The MSRC relies on sequencing of RNA extracted from blood; this assay displays high gene expression concordance between inter- and intra-assay sample (R² > 0.977) and minimal variation in cumulative gene assignment diversity, read mapping location, or gene-body coverage. The MSRC accuracy was 95.8% (46/48) for threshold concordance (no signal, high, very high). Intra- and inter-assay precision studies demonstrated high repeatability (92.6%, 25/27) and reproducibility (100%, 35/35).

CONCLUSION

The MSRC is a robust assay that accurately and reproducibly detects an RA patient's molecular signature of non-response to TNFi therapies.

Guided therapy selection in rheumatoid arthritis using a molecular signature response classifier: an assessment of budget impact and clinical utility

BACKGROUND: Patients with moderate to severe rheumatoid arthritis (RA) can be treated with a range of targeted therapies following inadequate response to conventional synthetic disease-modifying antirheumatic drugs such as methotrexate. Whereas clinical practice guidelines provide no formal recommendations for initial targeted therapies, the tumor necrosis factor alpha inhibitor (TNFi) class is the prevalent first-line selection based on clinician experience, its safety profile, and/or formulary requirements, while also being the costliest. Most patients do not achieve adequate clinical response with a first-line TNFi, however. A molecular signature response classifier (MSRC) test that assesses RA-related biomarkers can identify patients who are unlikely to achieve adequate response to TNFi-class therapies. OBJECTIVE: To model cost-effectiveness of MSRC-guided, first-line targeted therapy selection compared with current standard care. METHODS: This budget impact analysis used data sourced from August to September 2020. The prevalence of each first-line targeted therapy was obtained using market intelligence from Datamonitor/Informa PLC Rheumatology Dashboard Forecast 2020, and the average first-year cost of treatment for each class was calculated using wholesale acquisition costs from IBM Micromedex RED BOOK Online. Average effectiveness for each class was based on manufacturer-reported ACR50 response rates (American College of Rheumatology adequate response criteria of 50% improvement at 6 months after therapy initiation). The impact of MSRC testing on first therapy selection was predicted based on a third party-generated decision-impact study that analyzed potential alterations in rheumatologist prescribing patterns after receiving MSRC test reports. Sensitivity analysis evaluated potential impacts of variation in first-year medication cost, adherence to MSRC report, and test price on the first-year cost of treatment. Cost for response (first-year therapy cost therapy divided by probability of achieving ACR50) was compared between standard care and MSRC-guided care. RESULTS: The estimated cost for first-year, standard-care treatment was $65,117, with 80% of patients initiating treatment with a TNFi. Cost for achieving ACR50 response was $177,046. After applying MSRC-guided patient stratification and therapy selection, the first-year cost was $56,543, net of test price, with 49.0% of patients initiating with a TNFi. First-year MSRC-guided care cost, including test price, was estimated at $117,103, a 33.9% improvement over standard care. Sensitivity analysis showed a net cost improvement for guided care vs standard care across all scenarios. Patients predicted to be inadequate TNFi responders, when modeled with lower-priced alternatives, were predicted to show increased ACR50 response rates. Those with MSRC test results indicating a first-line TNFi were predicted to show an ACR50 response rate superior to that for any other class. In this model, if implemented clinically, MSRC-guided care might save the US health care system more than $850 million annually and improve ACR50 by up to 31.3%. CONCLUSIONS: Precision medicine using MSRC-guided patient stratification and therapy selection may both decrease cost and improve efficacy of targeted RA therapies. DISCLOSURES: This work was funded in full by Scipher Medicine Corporation, which participated in data analysis and interpretation and drafting, reviewing, and approving the publication. All authors contributed to data analysis and interpretation and publication preparation, maintaining control over the final content. Arnell, Withers, and Connolly-Strong are employees of and have stock ownership in Scipher Medicine Corporation. Bergman has received consulting fees from AbbVie, Gilead, GlaxoSmithKline, Novartis, Pfizer, Regeneron, Sanofi, and Scipher Medicine and owns stock or stock options in Johnson & Johnson. Kenney, Logan, and Lim-Harashima are consultants for Scipher Medicine Corporation. Basu has nothing to disclose.

A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study

Introduction

Timely matching of patients to beneficial targeted therapy is an unmet need in rheumatoid arthritis (RA). A molecular signature response classifier (MSRC) that predicts which patients with RA are unlikely to respond to tumor necrosis factor-α inhibitor (TNFi) therapy would have wide clinical utility.

Methods

The protein–protein interaction map specific to the rheumatoid arthritis pathophysiology and gene expression data in blood patient samples was used to discover a molecular signature of non-response to TNFi therapy. Inadequate response predictions were validated in blood samples from the CERTAIN cohort and a multicenter blinded prospective observational clinical study (NETWORK-004) among 391 targeted therapy-naïve and 113 TNFi-exposed patient samples. The primary endpoint evaluated the ability of the MSRC to identify patients who inadequately responded to TNFi therapy at 6 months according to ACR50. Additional endpoints evaluated the prediction of inadequate response at 3 and 6 months by ACR70, DAS28-CRP, and CDAI.

Results

The 23-feature molecular signature considers pathways upstream and downstream of TNFα involvement in RA pathophysiology. Predictive performance was consistent between the CERTAIN cohort and NETWORK-004 study. The NETWORK-004 study met primary and secondary endpoints. A molecular signature of non-response was detected in 45% of targeted therapy-naïve patients. The MSRC had an area under the curve (AUC) of 0.64 and patients were unlikely to adequately respond to TNFi therapy according to ACR50 at 6 months with an odds ratio of 4.1 (95% confidence interval 2.0–8.3, p value 0.0001). Odds ratios (3.4–8.8) were significant (p value < 0.01) for additional endpoints at 3 and 6 months, with AUC values up to 0.74. Among TNFi-exposed patients, the MSRC had an AUC of up to 0.83 and was associated with significant odds ratios of 3.3–26.6 by ACR, DAS28-CRP, and CDAI metrics.

Conclusion

The MSRC stratifies patients according to likelihood of inadequate response to TNFi therapy and provides patient-specific data to guide therapy choice in RA for targeted therapy-naïve and TNFi-exposed patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40744-021-00330-y.

A blood-based molecular signature response classifier (MSRC) integrating next-generation RNA sequencing data with clinical features predicts the likelihood that a patient with rheumatoid arthritis will have an inadequate response to TNFi therapy. Treatment selection guided by test results, with likely inadequate responders appropriately redirected to a different therapy, could improve response rates to TNFi therapies, generate healthcare cost savings, and increase rheumatologists’ confidence in prescribing decisions and altered treatment choices. The MSRC described in this study predicts the likelihood of inadequate response to TNFi therapies among targeted therapy-naïve and TNFi-exposed patients in a multicenter, 24-week blinded prospective clinical study: NETWORK-004. Patients with a molecular signature of non-response are less likely to have an adequate response to TNFi therapies than those patients lacking the signature according to ACR50, ACR70, CDAI, and DAS28-CRP with significant odds ratios of 3.4–8.8 for targeted therapy-naïve patients and 3.3–26.6 for TNFi-exposed patients. This MSRC provides a solution to the long-standing need for precision medicine tools to predict drug response in rheumatoid arthritis—a heterogeneous and progressive disease with an abundance of therapeutic options. These data validate the performance of the MSRC in a blinded prospective clinical study of targeted therapy-naïve and TNFi therapy-exposed patients.