Clinical Validation of a Blood-Based Predictive Test for Stratification of Response to Tumor Necrosis Factor Inhibitor Therapies in Rheumatoid Arthritis Patients

A proteomics study of rheumatoid arthritis patients on etanercept identifies putative biomarkers associated with clinical outcome measures

OBJECTIVES

Biologic DMARDs (bDMARDs) are widely used in patients with RA, but response to bDMARDs is heterogeneous. The objective of this work was to identify pretreatment proteomic biomarkers associated with RA clinical outcome measures in patients starting bDMARDs.

METHODS

Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) was used to generate spectral maps of sera from patients with RA before and after 3 months of treatment with the bDMARD etanercept. Protein levels were regressed against RA clinical outcome measures, i.e. 28-joint DAS (DAS28) and its subcomponents and DAS28 <2.6 (i.e. remission). The proteins with the strongest evidence for association were analysed in an independent, replication dataset. Finally, subnetwork analysis was carried out using the Disease Module Detection algorithm and biological plausibility of identified proteins was assessed by enrichment analysis.

RESULTS

A total of 180 patients with RA were included in the discovery dataset and 58 in the validation dataset from a UK-based prospective multicentre study. Ten individual proteins were found to be significantly associated with RA clinical outcome measures. The association of T-complex protein 1 subunit η with DAS28 remission was replicated in an independent cohort. Subnetwork analysis of the 10 proteins from the regression analysis identified the ontological theme, with the strongest associations being with acute phase and acute inflammatory responses.

CONCLUSION

This longitudinal study of 180 patients with RA commencing etanercept has identified several putative protein biomarkers of treatment response to this drug, one of which was replicated in an independent cohort.

Decision Impact Analysis to Measure the Influence of Molecular Signature Response Classifier Testing on Treatment Selection in Rheumatoid Arthritis

INTRODUCTION

Clinical guidelines offer little guidance for treatment selection following inadequate response to conventional synthetic disease-modifying antirheumatic drug (csDMARD) in rheumatoid arthritis (RA). A molecular signature response classifier (MSRC) was validated to predict tumor necrosis factor inhibitor (TNFi) inadequate response. The decision impact of MSRC results on biologic and targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD) selection was evaluated.

METHODS

This is an analysis of AIMS, a longitudinal, prospective database of patients with RA tested using the MSRC. This study assessed selection of b/tsDMARDs class after MSRC testing by surveying physicians, the rate of b/tsDMARD prescriptions aligning with MSRC results, and the percentage of physicians utilizing MSRC results for decision-making.

RESULTS

Of 1018 participants, 70.7% (720/1018) had treatment selected after receiving MSRC results. In this MSRC-informed cohort, 75.6% (544/720) of patients received a b/tsDMARD aligned with MSRC results, and 84.6% (609/720) of providers reported using MSRC results to guide treatment selection. The most prevalent reason reported (8.2%, 59/720) for not aligning treatment selection with MSRC results from the total cohort was health insurance coverage issues.

CONCLUSION

This study showed that rheumatologists reported using the MSRC test to guide b/tsDMARD selection for patients with RA. In most cases, MSRC test results appeared to influence clinical decision-making according to physician self-report. Wider adoption of precision medicine tools like the MSRC could support rheumatologists and patients in working together to achieve optimal outcomes for RA.

Noncoding RNAs improve the predictive power of network medicine

Network medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions (PPI), ignoring interactions mediated by noncoding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with PPI, constructing a comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases lacked a statistically significant disease module in the protein-based interactome but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including noncoding interactions improves both the breath and the predictive accuracy of network medicine.

Network medicine framework reveals generic herb-symptom effectiveness of traditional Chinese medicine

Understanding natural and traditional medicine can lead to world-changing drug discoveries. Despite the therapeutic effectiveness of individual herbs, traditional Chinese medicine (TCM) lacks a scientific foundation and is often considered a myth. In this study, we establish a network medicine framework and reveal the general TCM treatment principle as the topological relationship between disease symptoms and TCM herb targets on the human protein interactome. We find that proteins associated with a symptom form a network module, and the network proximity of an herb's targets to a symptom module is predictive of the herb's effectiveness in treating the symptom. These findings are validated using patient data from a hospital. We highlight the translational value of our framework by predicting herb-symptom treatments with therapeutic potential. Our network medicine framework reveals the scientific foundation of TCM and establishes a paradigm for understanding the molecular basis of natural medicine and predicting disease treatments.

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.

The module triad: a novel network biology approach to utilize patients' multi-omics data for target discovery in ulcerative colitis

Tumor necrosis factor-[Formula: see text] inhibitors (TNFi) have been a standard treatment in ulcerative colitis (UC) for nearly 20 years. However, insufficient response rate to TNFi therapies along with concerns around their immunogenicity and inconvenience of drug delivery through injections calls for development of UC drugs targeting alternative proteins. Here, we propose a multi-omic network biology method for prioritization of protein targets for UC treatment. Our method identifies network modules on the Human Interactome-a network of protein-protein interactions in human cells-consisting of genes contributing to the predisposition to UC (Genotype module), genes whose expression needs to be modulated to achieve low disease activity (Response module), and proteins whose perturbation alters expression of the Response module genes to a healthy state (Treatment module). Targets are prioritized based on their topological relevance to the Genotype module and functional similarity to the Treatment module. We demonstrate utility of our method in UC and other complex diseases by efficiently recovering the protein targets associated with compounds in clinical trials and on the market . The proposed method may help to reduce cost and time of drug development by offering a computational screening tool for identification of novel and repurposing therapeutic opportunities in UC and other complex diseases.

Patient outcomes improve when a molecular signature test guides treatment decision-making in rheumatoid arthritis

BACKGROUND

The molecular signature response classifier (MSRC) predicts tumor necrosis factor-ɑ inhibitor (TNFi) non-response in rheumatoid arthritis. This study evaluates decision-making, validity, and utility of MSRC testing.

METHODS

This comparative cohort study compared an MSRC-tested arm (N = 627) from the Study to Accelerate Information of Molecular Signatures (AIMS) with an external control arm (N = 2721) from US electronic health records. Propensity score matching was applied to balance baseline characteristics. Patients initiated a biologic/targeted synthetic disease-modifying antirheumatic drug, or continued TNFi therapy. Odds ratios (ORs) for six-month response were calculated based on clinical disease activity index (CDAI) scores for low disease activity/remission (CDAI-LDA/REM), remission (CDAI-REM), and minimally important differences (CDAI-MID) .

RESULTS

In MSRC-tested patients, 59% had a non-response signature and 70% received MSRC-aligned therapy . In TNFi-treated patients, the MSRC had an 88% PPV and 54% sensitivity. MSRC-guided patients were significantly (p < 0.0001) more likely to respond to b/tsDMARDs than those treated according to standard care (CDAI-LDA/REM: 36.0% vs 21.9%, OR 2.01[1.55-2.60]; CDAI-REM: 10.4% vs 3.6%, OR 3.14 [1.94-5.08]; CDAI-MID: 49.5% vs 32.8%, OR 2.01[1.58-2.55]).

CONCLUSION

MSRC clinical validity supports high clinical utility: guided treatment selection resulted in significantly superior outcomes relative to standard care; nearly three times more patients reached CDAI remission.

Network-based response module comprised of gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis

This cross-cohort study aimed to (1) determine a network-based molecular signature that predicts the likelihood of inadequate response to the tumor necrosis factor-ɑ inhibitor (TNFi) therapy, infliximab, in ulcerative colitis (UC) patients, and (2) address biomarker irreproducibility across different cohort studies. Whole-transcriptome microarray data were derived from biopsies of affected colon tissue from 2 cohorts of infliximab-treated UC patients (training N = 24 and validation N = 22). Response was defined as endoscopic and histologic healing at 4-6 weeks and 8 weeks, respectively. From the training cohort, genes with RNA expression that significantly correlated with clinical response outcomes were mapped onto the Human Interactome network map of protein-protein interactions to identify a largest connected component (LCC) of proteins indicative of infliximab response status in UC. Expression levels of transcripts within the LCC were fed into a probabilistic neural network model to generate a classifier that predicts inadequate response to infliximab. A classifier predictive of inadequate response to infliximab was generated and tested in a cross-cohort, blinded fashion; the AUC was 0.83 and inadequate response was predicted with a 100% positive predictive value and 64% sensitivity. Genes separately identified from the 2 cohorts that correlated with response to infliximab appeared distinct but mapped onto the same network region of the Human Interactome, reflecting a common underlying biology of response among UC patients. Cross-cohort validation of a classifier predictive of infliximab response status in UC patients indicates that a molecular signature of non-response to TNFi therapies is present in patients' baseline gene expression data. The goal is to develop a diagnostic test that predicts which patients will have an inadequate response to targeted therapies and define new targets and pathways for therapeutic development.

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.

Improvement in clinical disease activity index when treatment selection is informed by the tumor necrosis factor-ɑ inhibitor molecular signature response classifier: analysis from the study to accelerate information of molecular signatures in rheumatoid arthritis

BACKGROUND

A blood-based molecular signature response classifier (MSRC) predicts non-response to tumor necrosis factor-ɑ inhibitors (TNFi) in rheumatoid arthritis (RA).

RESEARCH DESIGN AND METHODS

This is an interim analysis of data collected in the Study to Accelerate Information of Molecular Signatures (AIMS) in RA from patients who received the MSRC test between September 2020 and November 2021. Absolute changes in clinical disease activity index (CDAI) scores from baseline were evaluated at 12 weeks (n = 470) and 24 weeks (n = 274).

RESULTS

Predicted TNFi non-responders who received a biologic or targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD) with an alternative mechanism of action (altMOA) experienced up to 1.8-fold greater improvements in CDAI scores than those treated with a TNFi (12 weeks: 12.2 vs 8.0; p-value = 0.083; 24 weeks: 14.2 vs 7.8 p-value = 0.009). In patients with a molecular signature of non-response to TNFi in high disease activity at baseline, this corresponded to 43.2% relative improvement in achieving a lower CDAI disease activity level when likely TNFi non-responders were treated with a non-TNFi therapy (38.9% vs 55.7%). Commensurate improvements in efficiency of spend are expected when TNFi are avoided in favor of altMOA.

CONCLUSIONS

RA treatment selection informed by MSRC test results improves clinical outcomes in real-world care and offers improvements in efficiency of healthcare spending.

A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets

Background

Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery.

Methods

We started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases.

Results

Our scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs.

Conclusions

We present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01048-4.

Biomarkers to Predict DMARDs Efficacy and Adverse Effect in Rheumatoid Arthritis

Rheumatoid arthritis (RA), one of the most common immune system diseases, mainly affects middle-aged and elderly individuals and has a serious impact on the quality of life of patients. Pain and disability caused by RA are significant symptoms negatively affecting patients, and they are especially seen when inappropriate treatment is administered. Effective therapeutic strategies have evolved over the past few decades, with many new disease-modifying antirheumatic drugs (DMARDs) being used in the clinic. Owing to the breakthrough in the treatment of RA, the symptoms of patients who could not be treated effectively in the past few years have been relieved. However, some patients complain about symptoms that have not been reported, implying that there are still some limitations in the RA treatment and evaluation system. In recent years, biomarkers, an effective means of diagnosing and evaluating the condition of patients with RA, have gradually been used in clinical practice to evaluate the therapeutic effect of RA, which is constantly being improved for accurate application of treatment in patients with RA. In this article, we summarize a series of biomarkers that may be helpful in evaluating the therapeutic effect and improving the efficiency of clinical treatment for RA. These efforts may also encourage researchers to devote more time and resources to the study and application of biomarkers, resulting in a new evaluation system that will reduce the inappropriate use of DMARDs, as well as patients’ physical pain and financial burden.

Integrating molecular interactions and gene expression to identify biomarkers to predict response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis patients

BACKGROUND:

Targeted therapy using anti-TNF (tumor necrosis factor) is the first option for patients with rheumatoid arthritis (RA). Anti-TNF therapy, however, does not lead to meaningful clinical improvement in many RA patients. To predict which patients will not benefit from anti-TNF therapy, clinical tests should be performed prior to treatment beginning.

OBJECTIVE:

Although various efforts have been made to identify biomarkers and pathways that may be helpful to predict the response to anti-TNF treatment, gaps remain in clinical use due to the low predictive power of the selected biomarkers.

METHODS:

In this paper, we used a network-based computational method to identify the select the predictive biomarkers to guide the treatment of RA patients.

RESULTS:

We select 69 genes from peripheral blood expression data from 46 subjects using a sparse network-based method. The result shows that the selected 69 genes might influence biological processes and molecular functions related to the treatment.

CONCLUSIONS:

Our approach advances the predictive power of anti-TNF therapy response and provides new genetic markers and pathways that may influence the treatment.

RETRACTED: Network-based response module comprised of gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the authors after consulting with the Editors. During a follow-up study, the authors regretfully discovered that the microarray probe-to-gene mapping was incorrect. Although the methodology and primary findings remain the same, the identity of the biomarker genes are incorrect as a result of this honest mistake. The extent of the changes to correct this information necessitated the publication of a corrected version of this article: https://doi.org/10.1016/j.trsl.2022.03.006.

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.

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.

A systems-based method to repurpose marketed therapeutics for antiviral use: a SARS-CoV-2 case study

This study describes two complementary methods that use network-based and sequence similarity tools to identify drug repurposing opportunities predicted to modulate viral proteins. This approach could be rapidly adapted to new and emerging viruses. The first method built and studied a virus-host-physical interaction network; a three-layer multimodal network of drug target proteins, human protein-protein interactions, and viral-host protein-protein interactions. The second method evaluated sequence similarity between viral proteins and other proteins, visualized by constructing a virus-host-similarity interaction network. Methods were validated on the human immunodeficiency virus, hepatitis B, hepatitis C, and human papillomavirus, then deployed on SARS-CoV-2. Comparison of virus-host-physical interaction predictions to known antiviral drugs had AUCs of 0.69, 0.59, 0.78, and 0.67, respectively, reflecting that the scores are predictive of effective drugs. For SARS-CoV-2, 569 candidate drugs were predicted, of which 37 had been included in clinical trials for SARS-CoV-2 (AUC = 0.75, P-value 3.21 × 10-3). As further validation, top-ranked candidate antiviral drugs were analyzed for binding to protein targets in silico; binding scores generated by BindScope indicated a 70% success rate.

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

INTRODUCTION

The PrismRA® test identifies rheumatoid arthritis (RA) patients who are unlikely to respond to anti-tumor necrosis factor (anti-TNF) therapies. This study evaluated the clinical and financial outcomes of incorporating PrismRA into routine clinical care of RA patients.

METHODS

A decision-analytic model was created to evaluate clinical and economic outcomes in the 12-month period following first biologic treatment. Two treatment strategies were compared: (1) observed clinical decision-making based on a 175-patient cohort receiving an anti-TNF therapy as their first biologic after failure of conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) and (2) modeled clinical decision-making of the same population using PrismRA results to inform first-line biologic treatment choice. Modeled costs include biologic drug pharmacy, non-biologic pharmacy, and total medical costs. The odds of inadequate response to anti-TNF therapies and various components of patient care were calculated based on PrismRA results.

RESULTS

Identifying predicted inadequate responders to anti-TNF therapies resulted in a modeled 38% increase in ACR50 response to first-line biologic therapies. The fraction of patients who achieved an ACR50 response to any therapy (TNFi and others) within the 12-month period was 33% higher in the PrismRA-stratified population than in the unstratified population (59 vs. 44%, respectively). When therapy prescriptions were modeled according to PrismRA results, cost savings were modeled for all financial variables: overall costs (4% decreased total, 19% decreased on ineffective treatments), total biologic drug pharmacy (4% total, 23% ineffective), non-biologic pharmacy (2% total, 19% ineffective), and medical costs (6% total, 19% ineffective). Female sex was the clinical metric that showed the greatest association with inadequate response to anti-TNF therapies (odds ratio 2.42, 95% confidence interval 1.20, 4.88).

CONCLUSIONS

If PrismRA is implemented into routine clinical care as modeled, predicting which RA patients will have an inadequate response to anti-TNF therapies could save > $7 million in overall ineffective healthcare costs per 1000 patients tested and increase targeted DMARD response rates in RA.