scholarly journals THU0022 DIFFERENTIAL DNA METHYLATION AS A PREDICTOR OF TOCILIZUMAB RESPONSE IN RHEUMATOID ARTHRITIS PATIENTS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 224.1-224
Author(s):  
N. Nair ◽  
D. Plant ◽  
J. Isaacs ◽  
A. Morgan ◽  
K. Hyrich ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Dna Methylation ◽  
Disease Activity ◽  
Whole Blood ◽  
Differential Methylation ◽  
Poor Responders ◽  
Research Support ◽  
Response Status ◽  
Pre Treatment ◽  
Methylation Patterns

Background:Tocilizumab (TCZ) is a biological disease-modifying antirheumatic drug that blocks IL-6 signalling and is effective in ameliorating disease activity in rheumatoid arthritis (RA). However, approximately 50% of patients do not respond adequately to TCZ and some patients report adverse events. Considering there is growing evidence that DNA methylation is implicated in RA susceptibility and response to some biologics (1, 2), we investigated DNA methylation as a candidate biomarker for response to TCZ in RA.Objectives:To identify differential DNA methylation signatures in whole blood associated with TCZ response in patients with RA.Methods:Epigenome-wide DNA methylation patterns were measured using the Infinium EPIC BeadChip (Illumina) in whole blood-derived DNA samples from patients with RA. DNA was extracted from blood samples taken pre-treatment and following 3 months on therapy, and response was determined at 6 months using the Clinical Disease Activity Index (CDAI). Patients who had good response (n=10) or poor response (n=10) to TCZ by 6 months were selected. Samples from secondary poor responders (n=10) (patients who had an improvement of CDAI and were in remission at 3 months, followed by a worsening of CDAI at 6 months) were also analysed. Differentially methylated positions and regions (DMPs/DMRs) were identified using linear regression, adjusting for gender, age, cell composition, smoking status, and glucocorticoid use. Gene Set Enrichment Analysis (GSEA) was used to identify significant pathways associated with response and Functional Epigenetic Module analysis of interactome hotspots in regions of differential methylation.Results:20 DMPs were significantly associated with response status at 6 months in the pre-treatment samples. Another 21 DMPs were associated with response in the 3 month samples. Within good responders, 10 DMPs showed significant change in methylation level between pre-treatment and the 3 month samples (unadjusted P-value <10-6). One DMP, cg03121467, was significantly less methylated in good responders compared to poor responders in the pre-treatment samples. This DMP is close toEPB41L4Aand thought to have a role in β–catenin signalling. GSEA of DMRs in non- and secondary non- responders identified histone acetyltransferase pathways and included theKAT2Agene, which is a repressor of NF-κB. Additional analysis of interaction hotspots of differential methylation identified significant interactions withSTAMBPandPTPN12associated with response status.Conclusion:These preliminary results provide evidence that DNA methylation patterns may predict response to TCZ. Validation of these findings in other larger data sets is required.References:[1]Liu,Y., Aryee,M.J., Padyukov,L., Fallin,M.D., Hesselberg,E., Runarsson,A., Reinius,L., Acevedo,N., Taub,M., Ronninger,M.,et al.(2013) Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis.Nat. Biotechnol.,31, 142–147.[2]Plant,D., Webster,A., Nair,N., Oliver,J., Smith,S.L., Eyre,S., Hyrich,K.L., Wilson,A.G., Morgan,A.W., Isaacs,J.D.,et al.(2016) Differential Methylation as a Biomarker of Response to Etanercept in Patients With Rheumatoid Arthritis.Arthritis Rheumatol. (Hoboken, N.J.),68, 1353–60.Disclosure of Interests:Nisha Nair: None declared, Darren Plant: None declared, John Isaacs Consultant of: AbbVie, Bristol-Myers Squibb, Eli Lilly, Gilead, Janssen, Merck, Pfizer, Roche, Ann Morgan Grant/research support from: I have received a grant from Roche Products Ltd to establish a registry for GCA patients treated with tocilizumab., Consultant of: I have undertaken consultancy work for Roche, Chugai, Regeneron, Sanofi and GSK in the area of GCA therapeutics., Speakers bureau: I have presented on tocilizumab therapy for GCA and glucocorticoid toxicity on behalf of Roche products ltd., Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Anne Barton Consultant of: AbbVie, Anthony G Wilson: None declared

scholarly journals POS0305 PHYSICIAN AND PATIENT ATTITUDES TOWARDS TREAT-TO-TARGET, ITS IMPLEMENTATION AND STATED TREATMENT GOALS IN PATIENTS WITH RHEUMATOID ARTHRITIS IN A REAL-WORLD SETTING ACROSS EUROPE

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 378-379
Author(s):  
B. Fautrel ◽  
R. Caporali ◽  
E. Holdsworth ◽  
B. Donaghy ◽  
M. Khalid ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Disease Activity ◽  
Real World ◽  
Current Treatment ◽  
High Disease Activity ◽  
Clinical State ◽  
Treat To Target ◽  
Treatment Goals ◽  
Research Support ◽  
Reduction Of Pain

Background:The principles of treat to target (T2T) include defining an appropriate treatment target, assessed at pre-defined intervals, with a commitment to changing therapeutic approach if the target is not met (1). T2T is recommended as a key strategy for the treatment of rheumatoid arthritis (RA).Objectives:To explore attitudes towards T2T, its implementation and stated treatment goals among physicians and their patients with RA.Methods:The Adelphi RA Disease Specific Programme™ was a large, quantitative, point-in-time survey conducted amongst rheumatologists (n=296) and their consulting patients with RA (n=3042) in Europe (France, Germany, Italy, Spain, UK) between Q4 2019–Q3 2020. Physicians were recruited via publicly available lists, completing an online survey and medical record extraction for their next 10–12 consecutive patients. The same patients were invited to voluntarily complete a self-report questionnaire (n=1098, 36% response), collecting data on attitudes towards T2T and treatment goals.Results:Physicians reported that 76% of patients were in remission (DAS28: <2.6) or had low disease activity (DAS28: 2.6 – 3.2), and 24% had moderate-high disease activity (DAS28: >3.2). Patient mean age was 53.0 years (SD 14.0), mean time since diagnosis was 7.2 years (SD 7.2). The proportion of patients currently receiving an advanced therapy (AT; defined as biologic or targeted synthetic DMARD) was 68%, of whom 70% were on a first line AT. No difference was observed between disease activity groups.In the physician survey, 86% of physicians stated they followed T2T principals in at least some of their RA patients, and would utilize a T2T approach in RA patients with moderate-high disease activity (61%), the most uncontrolled patients (37%) and those who do not respond well to initial therapy (34%). In this sample of real-world RA patients, 66% were reported by physicians to be on a T2T plan at the time of data collection. The most common physician-reported targets were remission (DAS28: <2.6) (75%), improvement of quality of life (QoL) (41%) and reduction of pain (31%), with 85% of physicians perceiving these treatment goals were fully or partially met. The most stated reasons for not implementing T2T was physician preference not to adjust current treatment (34%), patient preference not to adjust current treatment (23%), and there are no achievable goals for this patient (16%).Overall, 29% of patients reported they were involved in setting their T2T goals, while 34% stated their T2T goals were set by their physicians only, and 29% perceived no T2T goal had been set (n=620). The most common overall T2T goals from the patient perspective were remission (61%), controlling symptoms (41%), and reducing impact on QoL (34%). Of those patients who acknowledged a T2T goal had been set (n=407), 77% reported their T2T goal was fully or partially achieved.Of 719 patients who had moderate-high disease activity, 57% were on a T2T plan, with 46% of physicians perceiving these treatment goals were fully or partially met. The most common physician-stated reason for not implementing T2T was a lack of achievable targets (29%).Conclusion:Rheumatologists in this study reported a strong belief in T2T. The most common physician-set T2T goals were remission, improvement of QoL and reduction of pain, corresponding with T2T goals as reported by patients. However, a third of patients in this cohort were not aware of a defined T2T objective in their management, which may be a result of a perceived lack of achievable goals by physicians. It may be desirable to promote more patient involvement in defining achievable targets amongst those with moderate-high disease activity who despite best efforts may not reach a clinical state of remission. Further research is needed to identify and understand goals important to RA patients.References:[1]van Vollenhoven R. Treat-to-target in rheumatoid arthritis - are we there yet? Nat Rev Rheumatol. 2019;15(3):180-6.Acknowledgements:This study was funded by Galapagos NV, Belgium.Medical writing support was provided by Gary Sidgwick, PhD (Adelphi Real World, Bollington, UK) and editorial support was provided by Debbie Sherwood, BSc, CMPP (Aspire Scientific, Bollington, UK), both funded by Galapagos NV.Disclosure of Interests:Bruno Fautrel Consultant of: AbbVie, Amgen, Biogen, BMS, Celgene, Celltrion, Fresenius Kabi, Gilead, Janssen, Lilly, Medac, MSD, Mylan, NORDIC Pharma, Novartis, Pfizer, Roche, Sandoz, Sanofi-Genzyme, SOBI, UCB, Grant/research support from: AbbVie, Lilly, MSD, Pfizer, Roberto Caporali Speakers bureau: AbbVie, Amgen, Bristol Myers Squibb, Celltrion, Galapagos, Gilead, Lilly, Pfizer, Roche, UCB, Sanofi, Fresenius Kabi, Samsung Bioepis, MSD, Consultant of: Galapagos, Gilead, Lilly, Janssen, MSD, Elizabeth Holdsworth Employee of: Adelphi Real World, Bethany Donaghy Employee of: Adelphi Real World, Mona Khalid Shareholder of: Galapagos, Employee of: Galapagos, Mark Moore Shareholder of: Gilead Sciences, Speakers bureau: Gilead Sciences (only as employee), Paid instructor for: Gilead Sciences (only as employee), Consultant of: Gilead Sciences (only as employee), Grant/research support from: Gilead Sciences (only as employee), Employee of: Gilead Sciences, and previously Sanofi and AstraZeneca, Katrien Van Beneden Shareholder of: Galapagos, Employee of: Galapagos, Yves Piette Consultant of: AbbVie, Amgen, Galapagos, Grünenthal and Sandoz, Grant/research support from: Amgen, Mylan and UCB, Susana Romero-Yuste Speakers bureau: AbbVie, Amgen, Bristol Myers Squibb, Grunenthal, Kern Pharma, Lilly, Roche, Sandoz, Sanofi, UCB, Janssen, Consultant of: AbbVie, Biogen, Fresenius, Galapagos, Gebro, Janssen, Lilly, Grant/research support from: Bristol Myers Squibb, MSD, Novartis, Pfizer, Jasper Broen Shareholder of: Pharming Group, Consultant of: Galapagos, Gilead, Novartis, Peter C. Taylor Consultant of: AbbVie, Biogen, Galapagos, Gilead, GlaxoSmithKline, Janssen, Lilly, Pfizer, Roche, Sanofi, Nordic Pharma, Fresenius, UCB, Grant/research support from: Celgene, Galapagos, Gilead, Lilly

scholarly journals AB0330 HIGH REMISSION RATES IN RA – REAL LIFE DATA FROM BARITICINIB

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1463.2-1464
Author(s):  
S. Bayat ◽  
K. Tascilar ◽  
V. Kaufmann ◽  
A. Kleyer ◽  
D. Simon ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Disease Activity ◽  
Cox Regression ◽  
Real Life ◽  
Inadequate Response ◽  
Research Support ◽  
Das 28 ◽  
Patient Reported ◽  
One Year

Background:Recent developments of targeted treatments such as targeted synthetic DMARDs (tsDMARDs) increase the chances of a sustained low disease activity (LDA) or remission state for patients suffering rheumatoid arthritis (RA). tsDMARDs such as baricitinib, an oral inhibitor of the Janus Kinases (JAK1/JAK2) was recently approved for the treatment of RA with an inadequate response to conventional (cDMARD) and biological (bDMARD) therapy. (1, 2).Objectives:Aim of this study is to analyze the effect of baricitinb on disease activity (DAS28, LDA) in patients with RA in real life, to analyze drug persistance and associate these effects with various baseline characteristics.Methods:All RA patients were seen in our outpatient clinic. If a patient was switched to a baricitinib due to medical reasons, these patients were included in our prospective, observational study which started in April 2017. Clinical scores (SJC/TJC 76/78), composite scores (DAS28), PROs (HAQ-DI; RAID; FACIT), safety parameters (not reported in this abstract) as well as laboratory biomarkers were collected at each visit every three months. Linear mixed effects models for repeated measurements were used to analyze the time course of disease activity, patient reported outcomes and laboratory results. We estimated the probabilities of continued baricitinib treatment and the probabilities of LDA and remission by DAS-28 as well as Boolean remission up to one year using survival analysis and explored their association with disease characteristics using multivariable Cox regression. All patients gave informed consent. The study is approved by the local ethics.Results:95 patients were included and 85 analyzed with available follow-up data until November 2019. Demographics are shown in table 1. Mean follow-up duration after starting baricitinib was 49.3 (28.9) weeks. 51 patients (60%) were on monotherapy. Baricitinib survival (95%CI) was 82% (73% to 91%) at one year. Cumulative number (%probability, 95%CI) of patients that attained DAS-28 LDA at least once up to one year was 67 (92%, 80% to 97%) and the number of patients attaining DAS-28 and Boolean remission were 31 (50%, 34% to 61%) and 12(20%, 9% to 30%) respectively. Median time to DAS-28 LDA was 16 weeks (Figure 1). Cox regression analyses did not show any sufficiently precise association of remission or LDA with age, gender, seropositivity, disease duration, concomitant DMARD use and number of previous bDMARDs. Increasing number of previous bDMARDs was associated with poor baricitinib survival (HR=1.5, 95%CI 1.1 to 2.2) while this association was not robust to adjustment for baseline disease activity. Favorable changes were observed in tender and swollen joint counts, pain-VAS, patient and physician disease assessment scores, RAID, FACIT and the acute phase response.Conclusion:In this prospective observational study, we observed high rates of LDA and DAS-28 remission and significant improvements in disease activity and patient reported outcome measurements over time.References:[1]Keystone EC, Taylor PC, Drescher E, Schlichting DE, Beattie SD, Berclaz PY, et al. Safety and efficacy of baricitinib at 24 weeks in patients with rheumatoid arthritis who have had an inadequate response to methotrexate. Annals of the rheumatic diseases. 2015 Feb;74(2):333-40.[2]Genovese MC, Kremer J, Zamani O, Ludivico C, Krogulec M, Xie L, et al. Baricitinib in Patients with Refractory Rheumatoid Arthritis. The New England journal of medicine. 2016 Mar 31;374(13):1243-52.Figure 1.Cumulative probability of low disease activity or remission under treatment with baricitinib.Disclosure of Interests:Sara Bayat Speakers bureau: Novartis, Koray Tascilar: None declared, Veronica Kaufmann: None declared, Arnd Kleyer Consultant of: Lilly, Gilead, Novartis,Abbvie, Speakers bureau: Novartis, Lilly, David Simon Grant/research support from: Else Kröner-Memorial Scholarship, Novartis, Consultant of: Novartis, Lilly, Johannes Knitza Grant/research support from: Research Grant: Novartis, Fabian Hartmann: None declared, Susanne Adam: None declared, Axel Hueber Grant/research support from: Novartis, Lilly, Pfizer, EIT Health, EU-IMI, DFG, Universität Erlangen (EFI), Consultant of: Abbvie, BMS, Celgene, Gilead, GSK, Lilly, Novartis, Speakers bureau: GSK, Lilly, Novartis, Georg Schett Speakers bureau: AbbVie, BMS, Celgene, Janssen, Eli Lilly, Novartis, Roche and UCB

scholarly journals THU0207 SUSTAINABILITY OF RESPONSE TO UPADACITINIB AS MONOTHERAPY OR IN COMBINATION AMONG PATIENTS WITH RHEUMATOID ARTHRITIS AND PRIOR INADEQUATE RESPONSE TO CONVENTIONAL SYNTHETIC DMARDS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 327.1-328
Author(s):  
A. Kavanaugh ◽  
M. H. Buch ◽  
B. Combe ◽  
L. Bessette ◽  
I. H. Song ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Disease Activity ◽  
Inadequate Response ◽  
Research Support ◽  
Two Phase ◽  
First Occurrence ◽  
Activity Measures ◽  
Disease Activity Measures

Background:The primary treatment goal for patients (pts) with rheumatoid arthritis (RA) is a state of sustained clinical remission (REM) or low disease activity (LDA).1,2Objectives:To assess the long-term sustainability of responses to upadacitinib (UPA), a JAK inhibitor, with or without background csDMARD(s) in pts with RA.Methods:Data are from two phase 3 randomized, controlled trials of UPA in RA pts with roughly similar baseline disease characteristics: SELECT-NEXT enrolled pts with an inadequate response (IR) to csDMARD(s) on background stable csDMARD(s) receiving UPA 15 mg or 30 mg once daily or placebo for 12 weeks (wks); SELECT-MONOTHERAPY enrolled methotrexate (MTX)-IR pts receiving UPA 15 mg or 30 mg monotherapy or blinded MTX for 14 wks. After 12/14 wks, pts could enter a blinded long-term extension and receive UPA 15 mg or 30 mg for up to 5 years. This post hoc analysis evaluated clinical REM (CDAI ≤2.8; SDAI ≤3.3), LDA (CDAI≤10; SDAI≤11), and DAS28(CRP) <2.6/≤3.2 at first occurrence before Wk 84; additionally, these measures were evaluated at 3, 6, and 12 months after the first occurrence for the total number of pts randomized to UPA 15 mg. Sustainability of response was evaluated by Kaplan-Meier only for those pts who achieved REM/LDA and was defined as time to the earliest date of losing response at two consecutive visits or discontinuation of study drug. The predictive ability of time to clinical REM/LDA was assessed using Harrell’s concordance (c)-index (for reference, an index ~ 0.5, indicates no ability to predict; an index of 1 or -1 would be a perfect prediction). The last follow up dates were 22 March, 2018 (SELECT-NEXT) and 25 May, 2019 (SELECT-MONOTHERAPY), when all pts had reached the Wk 84 visit.Results:Through Wk 84, the percent of treated pts achieving CDAI REM/LDA was 43%/79% for those receiving UPA 15 mg with background csDMARD(s) (SELECT-NEXT) and 37%/76% for those receiving UPA 15 mg without background csDMARD(s) (SELECT-MONOTHERAPY). 35%/25% of pts randomized to UPA 15 mg with background csDMARD(s) and 27%/23% of pts randomized to UPA 15 mg without background csDMARD(s) achieved sustained CDAI REM through 6/12 months after the first occurrence. 64%/56% of pts randomized to UPA 15 mg with background csDMARD(s) and 61%/56% of pts randomized to UPA 15 mg without background csDMARD(s) achieved sustained CDAI LDA through 6/12 months after the first occurrence (Figure 1). Time to initial clinical REM/LDA did not appear to be associated with sustained disease control. The c-indices (95%CI) for CDAI REM in the UPA 15 mg with background csDMARD(s) and UPA 15 mg without background csDMARD(s) groups were 0.541 (0.47, 0.62) and 0.568 (0.49, 0.65) and that of LDA were 0.521 (0.46, 0.58) and 0.498 (0.43, 0.56), respectively. Through last follow-up visit, 55% of pts receiving UPA 15 mg with background csDMARD(s) and 62% of pts receiving UPA 15 mg without background csDMARD(s) remained in CDAI REM while 72% and 70% of pts remained in CDAI LDA, respectively (Figure 2). Similar results were observed across other disease activity measures (SDAI REM/LDA and DAS28(CRP) <2.6/≤3.2).Conclusion:More than a quarter and more than a half of pts with RA and prior IR to csDMARD(s) receiving UPA with or without background csDMARD therapy achieved sustained clinical REM and LDA, respectively, across disease activity measures. Sustainability of responses appeared comparable among pts receiving UPA with or without background csDMARDs through up to 84 wks.References:[1]EULAR: Smolen JS, et al. Ann Rheum Dis 2017;76:960–977.[2]ACR: Singh et al. Arthritis & Rheumatology Vol. 68, No. 1, January 2016, pp 1–26.Disclosure of Interests: :Arthur Kavanaugh Grant/research support from: Abbott, Amgen, AstraZeneca, BMS, Celgene Corporation, Centocor-Janssen, Pfizer, Roche, UCB – grant/research support, Maya H Buch Grant/research support from: Pfizer, Roche, and UCB, Consultant of: Pfizer; AbbVie; Eli Lilly; Gilead Sciences, Inc.; Merck-Serono; Sandoz; and Sanofi, Bernard Combe Grant/research support from: Novartis, Pfizer, Roche-Chugai, Consultant of: AbbVie; Gilead Sciences, Inc.; Janssen; Eli Lilly and Company; Pfizer; Roche-Chugai; Sanofi, Speakers bureau: Bristol-Myers Squibb; Gilead Sciences, Inc.; Eli Lilly and Company; Merck Sharp & Dohme; Pfizer; Roche-Chugai; UCB, Louis Bessette Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, Roche, Sanofi, UCB Pharma, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, Roche, Sanofi, UCB Pharma, Speakers bureau: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, Sanofi, In-Ho Song Shareholder of: AbbVie Inc., Employee of: AbbVie Inc., Yanna Song Shareholder of: AbbVie Inc., Employee of: AbbVie Inc., Jessica Suboticki Shareholder of: AbbVie Inc., Employee of: AbbVie Inc., Peter Nash Grant/research support from: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Gilead, Janssen, MSD, Novartis, Pfizer Inc, Roche, Sanofi, UCB, Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, MSD, Novartis, Pfizer Inc, Roche, Sanofi, UCB, Speakers bureau: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, MSD, Novartis, Pfizer Inc, Roche, Sanofi, UCB

scholarly journals AB0140 A HIGH-CONFIDENCE DEFINITION OF THERAPEUTIC RESPONSE IN RHEUMATOID ARTHRITIS USING A MONTE CARLO SIMULATION APPROACH

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1097.2-1098
Author(s):  
V. Strand ◽  
S. Cohen ◽  
L. Zhang ◽  
T. Mellors ◽  
A. Jones ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Monte Carlo ◽  
Disease Activity ◽  
Health Assessment Questionnaire ◽  
Health Assessment ◽  
High Confidence ◽  
Response Status ◽  
Definition Of ◽  
Fidelity Score ◽  
Assessment Questionnaire

Background:Therapy choice and therapy change depend on the ability to accurately assess patients’ disease activity. The clinical assessments used to evaluate treatment response in rheumatoid arthritis have inherent variability, normally considered as measurement error, intra-observer variability or within subject variability. Each contribute to variability in deriving response status as defined by composite measures such as the ACR or EULAR criteria, particularly when a one-time observed measurement lies near the boundary defining response or non-response. To select an optimal therapeutic strategy in the burgeoning age of precision medicine in rheumatology, achieve the lowest disease activity and maximize long-term health outcomes for each patient, improved treatment response definitions are needed.Objectives:Develop a high-confidence definition of treatment response and non-response in rheumatoid arthritis that exceeds the expected variability of subcomponents in the composite response criteria.Methods:A Monte Carlo simulation approach was used to assess ACR50 and EULAR response outcomes in 100 rheumatoid arthritis patients who had been treated for 6 months with a TNF inhibitor therapy. Monte Carlo simulations were run with 2000 iterations implemented with measurement variability derived for each clinical assessment: tender joint count, swollen joint count, Health Assessment Questionnaire disability index (HAQ-DI), patient pain assessment, patient global assessment, physician global assessment, serum C-reactive protein level (CRP) and disease activity score 28-joint count with CRP.1-3 Each iteration of the Monte Carlo simulation generated one outcome with a value of 0 or 1 indicating non-responder or responder, respectively.Results:A fidelity score, calculated separately for ACR50 and EULAR response, was defined as an aggregated score from 2000 iterations reported as a fraction that ranges from 0 to 1. The fidelity score depicted a spectrum of response covering strong non-responders, inconclusive statuses and strong responders. A fidelity score around 0.5 typified a response status with extreme variability and inconclusive clinical response to treatment. High-fidelity scores were defined as >0.7 or <0.3 for responders and non-responders, respectively, meaning that the simulated clinical response status label among all simulations agreed at least 70% of the time. High-confidence true responders were considered as those patients with high-fidelity outcomes in both ACR50 and EULAR outcomes.Conclusion:A definition of response to treatment should exceed the expected variability of the clinical assessments used in the composite measure of therapeutic response. By defining high-confidence responders and non-responders, the true impact of therapeutic efficacy can be determined, thus forging a path to development of better treatment options and advanced precision medicine tools in rheumatoid arthritis.References:[1]Cheung, P. P., Gossec, L., Mak, A. & March, L. Reliability of joint count assessment in rheumatoid arthritis: a systematic literature review. Semin Arthritis Rheum43, 721-729, doi:10.1016/j.semarthrit.2013.11.003 (2014).[2]Uhlig, T., Kvien, T. K. & Pincus, T. Test-retest reliability of disease activity core set measures and indices in rheumatoid arthritis. Ann Rheum Dis68, 972-975, doi:10.1136/ard.2008.097345 (2009).[3]Maska, L., Anderson, J. & Michaud, K. Measures of functional status and quality of life in rheumatoid arthritis: Health Assessment Questionnaire Disability Index (HAQ), Modified Health Assessment Questionnaire (MHAQ), Multidimensional Health Assessment Questionnaire (MDHAQ), Health Assessment Questionnaire II (HAQ-II), Improved Health Assessment Questionnaire (Improved HAQ), and Rheumatoid Arthritis Quality of Life (RAQoL). Arthritis Care Res (Hoboken) 63 Suppl 11, S4-13, doi:10.1002/acr.20620 (2011).Disclosure of Interests:Vibeke Strand Consultant of: Abbvie, Amgen, Arena, BMS, Boehringer Ingelheim, Celltrion, Galapagos, Genentech/Roche, Gilead, GSK, Ichnos, Inmedix, Janssen, Kiniksa, Lilly, Merck, Novartis, Pfizer, Regeneron, Samsung, Sandoz, Sanofi, Setpoint, UCB, Stanley Cohen: None declared, Lixia Zhang Shareholder of: Scipher Medicine Corporation, Employee of: Scipher Medicine Corporation, Ted Mellors Shareholder of: Scipher Medicine Corporation, Employee of: Scipher Medicine Corporation, Alex Jones Shareholder of: Scipher Medicine Corporation, Employee of: Scipher Medicine Corporation, Johanna Withers Shareholder of: Scipher Medicine Corporation, Employee of: Scipher Medicine Corporation, Viatcheslav Akmaev Shareholder of: Scipher Medicine Corporation, Employee of: Scipher Medicine Corporation

scholarly journals SAT0039 ADAPTIVE DEEP LEARNING FOR THE PREDICTION OF INDIVIDUAL DISEASE ACTIVITY IN PATIENTS WITH RHEUMATOID ARTHRITIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 950.1-950
Author(s):  
M. Hügle ◽  
G. Kalweit ◽  
U. Walker ◽  
A. Finckh ◽  
R. Muller ◽  
...  
Keyword(s):  
Neural Network ◽  
Rheumatoid Arthritis ◽  
Disease Activity ◽  
Deep Neural Networks ◽  
Disease Duration ◽  
Research Support ◽  
Disease Characteristics ◽  
Individual Disease ◽  
Active Disease ◽  
Research Grant

Background:Rheumatoid arthritis (RA) lacks reliable biomarkers that predict disease evolution on an individual basis, potentially leading to over- and undertreatment. Deep neural networks learn from former experiences on a large scale and can be used to predict future events as a potential tool for personalized clinical assistance.Objectives:To investigate deep learning for the prediction of individual disease activity in RA.Methods:Demographic and disease characteristics from over 9500 patients with 65.000 visits from the Swiss Quality Management (SCQM) database were used to train and evaluate an adaptive recurrent neural network (AdaptiveNet). Patient and disease characteristics along with clinical and patient reported outcomes, laboratory values and medication were used as input features. DAS28-BSR was used to predict active disease and future numeric individual disease activity by classification and regression, respectively.Results:AdaptiveNet predicted active disease defined as DAS28-BSR>2.6 at the next visit, with an overall accuracy of 75.6% and a sensitivity and specificity of 84.2% and 61.5%, respectively. Apart from DAS28-BSR, the most influential characteristics to predict disease activity were joint pain, disease duration, age and medication. Longer disease duration, age >50 or antibody positivity marginally improved prediction performance. Regression allowed forecasting individual DAS28-BSR values with a mean squared error of 0.9.Conclusion:Deep neural networks have the capacity to predict individual disease outcome in RA. Low specificity remains challenging and might benefit from alternative input data or outcome targets.References:[1] Hügle M, Kalweit G, Hügle T, Boedecker J. A Dynamic Deep Neural Network For Multimodal Clinical Data Analysis. Be Publ Stud Comput Intell Springer Verl. 2020.Figure 1.Examples of true disease activity and corresponding predictions of AdaptiveNet by regression analysis. Predictions are made step to step from the current to next visit.Disclosure of Interests:Maria Hügle Paid instructor for: Lilly, Gabriel Kalweit: None declared, Ulrich Walker Grant/research support from: Ulrich Walker has received an unrestricted research grant from Abbvie, Consultant of: Ulrich Walker has act as a consultant for Abbvie, Actelion, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, MSD, Novartis, Pfizer, Phadia, Roche, Sandoz, Sanofi, and ThermoFisher, Paid instructor for: Abbvie, Novartis, and Roche, Speakers bureau: Abbvie, Actelion, Bristol-Myers Squibb, Celgene, MSD, Novartis, Pfizer, Phadia, Roche, Sandoz, and ThermoFisher, Axel Finckh Grant/research support from: Pfizer: Unrestricted research grant, Eli-Lilly: Unrestricted research grant, Consultant of: Sanofi, AB2BIO, Abbvie, Pfizer, MSD, Speakers bureau: Sanofi, Pfizer, Roche, Thermo Fisher Scientific, Rudiger Muller Consultant of: AbbVie, Nordic, Sandoz, Almut Scherer: None declared, Joschka Boedecker: None declared, Thomas Hügle Grant/research support from: Abbvie, Novartis, Consultant of: Abbvie, Pfizer, Novartis, Roche, Lilly, BMS

scholarly journals AB0337 TOFACITINIB MONOTHERAPY OR COMBINED WITH METHOTREXATE IN PATIENTS WITH RHEUMATOID ARTHRITIS SHOW SIMILAR RETENTION OVER FOUR YEARS. REPORT FROM RHUMADATA ®

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1467.1-1467
Author(s):  
D. Choquette ◽  
L. Bessette ◽  
L. Choquette Sauvageau ◽  
I. Ferdinand ◽  
B. Haraoui ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Disease Activity ◽  
Real World ◽  
Treatment Initiation ◽  
Retention Rate ◽  
Janus Kinase ◽  
P Value ◽  
Research Support ◽  
Medication Discontinuation ◽  
Kaplan Meier

Background:Since the introduction of biologic agents around the turn of the century, the scientific evidence shows that the majority of agents, independent of the therapeutic target, have a better outcome when used in combination with methotrexate (MTX). In 2014, tofacitinib (TOFA), an agent targeting Janus kinase 1 and 3, has reached the Canadian market with data showing that the combination with MTX may not be necessary [1,2].Objectives:To evaluate the efficacy and retention rate of TOFA in real-world patients with rheumatoid arthritis (RA).Methods:Two cohorts of patients prescribed TOFA was created. The first cohort was formed of patients who were receiving MTX concomitantly with TOFA (COMBO) and the other of patients using TOFA in monotherapy (MONO). MONO patients either never use MTX or were prescribed MTX post-TOFA initiation for at most 20% of the time they were on TOFA. COMBO patients received MTX at the time of TOFA initiation or were prescribed MTX post-TOFA initiation for at least 80% of the time. For all those patients, baseline demographic data definitions. Disease activity score and HAQ-DI were compared from the initiation of TOFA to the last visit. Time to medication discontinuation was extracted, and survival was estimated using Kaplan-Meier calculation for MONO and COMBO cohorts.Results:Overall, 194 patients were selected. Most were women (83%) on average younger than the men (men: 62.6 ± 11.0 years vs. women: 56.9 ± 12.1 years, p-value=0.0130). The patient’s assessments of global disease activity, pain and fatigue were respectively 5.0 ± 2.7, 5.2 ± 2.9, 5.1 ± 3.1 in the COMBO group and 6.2 ± 2.5, 6.5 ± 2.6, 6.3 ± 2.8 in the MONO group all differences being significant across groups. HAQ-DI at treatment initiation was 1.3 ± 0.7 and 1.5 ± 0.7 in the COMBO and MONO groups, respectively, p-value=0.0858. Similarly, the SDAI score at treatment initiation was 23.9 ± 9.4 and 25.2 ± 11.5, p-value=0.5546. Average changes in SDAI were -13.4 ± 15.5 (COMBO) and -8.9 ± 13.5 (MONO), p-value=0.1515, and changes in HAQ -0.21 ± 0.63 and -0.26 ± 0.74, p-value 0.6112. At treatment initiation, DAS28(4)ESR were 4.4 ± 1.4 (COMBO) and 4.6 ± 1.3 (MONO), p-value 0.5815, with respective average changes of -1.06 ± 2.07 and -0.70 ± 1.96, p-value=0.2852. The Kaplan-Meier analysis demonstrated that the COMBO and MONO retention curves were not statistically different (log-rank p-value=0.9318).Conclusion:Sustainability of TOFA in MONO or COMBO are not statistically different as are the changes in DAS28(4)ESR and SDAI. Despite this result, some patients may still benefit from combination with MTX.References:[1]Product Monograph - XELJANZ ® (tofacitinib) tablets for oral administration Initial U.S. Approval: 2012.[2] Reed GW, Gerber RA, Shan Y, et al. Real-World Comparative Effectiveness of Tofacitinib and Tumor Necrosis Factor Inhibitors as Monotherapy and Combination Therapy for Treatment of Rheumatoid Arthritis [published online ahead of print, 2019 Nov 9].Rheumatol Ther. 2019;6(4):573–586. doi:10.1007/s40744-019-00177-4.Disclosure of Interests:Denis Choquette Grant/research support from: Rhumadata is supported by grants from Pfizer, Amgen, Abbvie, Gylead, BMS, Novartis, Sandoz, eli Lilly,, Consultant of: Pfizer, Amgen, Abbvie, Gylead, BMS, Novartis, Sandoz, eli Lilly,, Speakers bureau: Pfizer, Amgen, Abbvie, Gylead, BMS, Novartis, Sandoz, eli Lilly,, Louis Bessette Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, Roche, Sanofi, UCB Pharma, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, Roche, Sanofi, UCB Pharma, Speakers bureau: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, Sanofi, Loïc Choquette Sauvageau: None declared, Isabelle Ferdinand Consultant of: Pfizer, Abbvie, Amgen, Novartis, Speakers bureau: Pfizer, Amgen, Boulos Haraoui Grant/research support from: Abbvie, Amgen, Pfizer, UCB, Grant/research support from: AbbVie, Amgen, BMS, Janssen, Pfizer, Roche, and UCB, Consultant of: Abbvie, Amgen, Lilly, Pfizer, Sandoz, UCB, Consultant of: AbbVie, Amgen, BMS, Celgene, Eli Lilly, Janssen, Merck, Pfizer, Roche, and UCB, Speakers bureau: Pfizer, Speakers bureau: Amgen, BMS, Janssen, Pfizer, and UCB, Frédéric Massicotte Consultant of: Abbvie, Janssen, Lilly, Pfizer, Speakers bureau: Janssen, Jean-Pierre Pelletier Shareholder of: ArthroLab Inc., Grant/research support from: TRB Chemedica, Speakers bureau: TRB Chemedica and Mylan, Jean-Pierre Raynauld Consultant of: ArthroLab Inc., Marie-Anaïs Rémillard Consultant of: Abbvie, Amgen, Eli Lilly, Novartis, Pfizer, Sandoz, Paid instructor for: Abbvie, Amgen, Eli Lilly, Novartis, Pfizer, Sandoz, Speakers bureau: Abbvie, Amgen, Eli Lilly, Novartis, Pfizer, Sandoz, Diane Sauvageau: None declared, Édith Villeneuve Consultant of: Abbvie, Amgen, BMS, Celgene, Pfizer, Roche, Sanofi-Genzyme,UCB, Paid instructor for: Abbvie, Speakers bureau: AbbVie, BMS, Pfizer, Roche, Louis Coupal: None declared

scholarly journals FRI0020 CLINICAL TREATMENT RESPONSE STILL DOES NOT MATCH PATIENT REPORTED IMPROVEMENT, EVEN IN EARLY RHEUMATOID ARTHRITIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 582.1-582
Author(s):  
S. Pazmino ◽  
A. Lovik ◽  
A. Boonen ◽  
D. De Cock ◽  
V. Stouten ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Disease Activity ◽  
Early Rheumatoid Arthritis ◽  
Health Assessment ◽  
Sustained Remission ◽  
Research Support ◽  
Early Ra ◽  
Severity Scores ◽  
Patient Reported ◽  
Over Time

Background:Commonly used disease activity scores in rheumatoid arthritis (RA) include one patient reported outcome (PRO) -the patient’s global health assessment (PGA). Exploratory factor analysis (EFA) was performed on data from the 2 year Care in early Rheumatoid Arthritis (CareRA) trial to explain the evolution of disease burden extracting 3 factors.1Objectives:To assess the evolution and relative responsiveness over time of clinical, laboratory and patient assessments included in composite scores, together with other PROs like pain, fatigue and functionality in patients with early RA (≤1 year) treated to target (T2T) within the CareRA trial.Methods:DMARD naïve patients with early RA (n=379) were included, randomized to remission induction with COBRA-like treatment schemes (n=332) or MTX monotherapy (n=47) and T2T.Components of disease activity scores (swollen/tender joint count (S/TJC), C-reactive protein (CRP) or erythrocyte sedimentation rate (ESR), and physician (PhGH) or patient (PGA) global health assessment), pain and fatigue (both on 0-100 scale) and HAQ were recorded at every visit.Missing data was handled with multiple imputation (n=15). Clustering was removed with multiple outputation (n=1000), then each of the 15 000 datasets was analyzed by EFA with principal component extraction and oblimin rotation. The analyses were combined after re-ordering the factors by maximizing factor congruence. The 3 extracted factors and their individual components (with their loadings) were: 1. Patient containing PGA (0.87), pain (0.86), fatigue (0.90) and HAQ (0.5) 2.Clinical with SJC (0.92), TJC (0.89) and PhGH (0.76) and 3.Laboratory with CRP(0.87) and ESR (0.78).1(Pazmino, ACR 2019 abstract, Table 3)Afterwards, variables were first normalized to a 0-1 scale, then multiplied -weighted- by the factor loadings previously obtained.1For each Patient, Clinical and Laboratory severity score, the weighted variables belonging to each score were summed together and then re-scaled to 0-1 (higher values suggest more burden).The percentage (%) improvement from baseline to week 104 and the area under the curve (AUC) across time points were calculated per factor.Differences in % improvement and AUC were compared between patients not achieving and achieving early and sustained (week 16 to 104) disease activity score remission (DAS28CRP <2.6) with ANOVA. Bonferroni correction was used for multiple testing.Results:Severity scores of Patient, Clinical and Laboratory factors improved rapidly over time (Figure 1). In patients achieving sustained remission (n=122), Patient, Clinical and Laboratory scores improved 56%, 90% and 27% respectively. In patients not achieving sustained remission (n=257) the improvement was 32%, 78% and 9% respectively (p<0.001 only for clinical improvement).Patients in CareRA who achieved sustained remission had an AUC of 15.1, 3.4 and 4.7 in Patient, Clinical and Laboratory scores respectively, compared to 32.3, 10.0, and 7.2 in participants not achieving sustained remission (p<0.001 for all comparisons).Conclusion:Patient, Clinical and Laboratory severity scores improved rapidly over time in patients achieving rapid and sustained disease control. However, overall, Patient burden seemed not to improve to the same extent as Clinical burden. Patient’s unmet needs in terms of pain, fatigue, functionality and overall well-being should thus be given more attention, even in patients in sustained remission.References:[1]Pazmino S,et al.Including Pain, Fatigue and Functionality Regularly in the Assessment of Patients with Early Rheumatoid Arthritis Separately Adds to the Evaluation of Disease Status [abstract]. ACR. 2019.Disclosure of Interests:Sofia Pazmino: None declared, Anikó Lovik: None declared, Annelies Boonen Grant/research support from: AbbVie, Consultant of: Galapagos, Lilly (all paid to the department), Diederik De Cock: None declared, Veerle Stouten: None declared, Johan Joly: None declared, Delphine Bertrand: None declared, Rene Westhovens Grant/research support from: Celltrion Inc, Galapagos, Gilead, Consultant of: Celltrion Inc, Galapagos, Gilead, Speakers bureau: Celltrion Inc, Galapagos, Gilead, Patrick Verschueren Grant/research support from: Pfizer unrestricted chair of early RA research, Speakers bureau: various companies

scholarly journals Exploratory analysis of age and sex dependent DNA methylation patterns on the X-chromosome in whole blood samples

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Shuxia Li ◽  
Jesper B. Lund ◽  
Kaare Christensen ◽  
Jan Baumbach ◽  
Jonas Mengel-From ◽  
...  
Keyword(s):  
Dna Methylation ◽  
X Chromosome ◽  
Whole Blood ◽  
Exploratory Analysis ◽  
Blood Samples ◽  
Whole Blood Samples ◽  
Methylation Patterns ◽  
Age And Sex

Genome-Wide DNA Methylation Analysis Shows Enrichment of Differential Methylation in “Open Seas” and Enhancers and Reveals Hypomethylation in DNMT3A Mutated Cytogenetically Normal AML (CN-AML)

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 653-653 ◽  
Author(s):  
Ying Qu ◽  
Andreas Lennartsson ◽  
Verena I. Gaidzik ◽  
Stefan Deneberg ◽  
Sofia Bengtzén ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Cpg Island ◽  
Cpg Islands ◽  
Differential Methylation ◽  
Methylation Analysis ◽  
Cpg Sites ◽  
Genome Wide ◽  
Genomic Regions ◽  
Methylation Patterns

Abstract Abstract 653 DNA methylation is involved in multiple biologic processes including normal cell differentiation and tumorigenesis. In AML, methylation patterns have been shown to differ significantly from normal hematopoietic cells. Most studies of DNA methylation in AML have previously focused on CpG islands within the promoter of genes, representing only a very small proportion of the DNA methylome. In this study, we performed genome-wide methylation analysis of 62 AML patients with CN-AML and CD34 positive cells from healthy controls by Illumina HumanMethylation450K Array covering 450.000 CpG sites in CpG islands as well as genomic regions far from CpG islands. Differentially methylated CpG sites (DMS) between CN-AML and normal hematopoietic cells were calculated and the most significant enrichment of DMS was found in regions more than 4kb from CpG Islands, in the so called open sea where hypomethylation was the dominant form of aberrant methylation. In contrast, CpG islands were not enriched for DMS and DMS in CpG islands were dominated by hypermethylation. DMS successively further away from CpG islands in CpG island shores (up to 2kb from CpG Island) and shelves (from 2kb to 4kb from Island) showed increasing degree of hypomethylation in AML cells. Among regions defined by their relation to gene structures, CpG dinucleotide located in theoretic enhancers were found to be the most enriched for DMS (Chi χ2<0.0001) with the majority of DMS showing decreased methylation compared to CD34 normal controls. To address the relation to gene expression, GEP (gene expression profiling) by microarray was carried out on 32 of the CN-AML patients. Totally, 339723 CpG sites covering 18879 genes were addressed on both platforms. CpG methylation in CpG islands showed the most pronounced anti-correlation (spearman ρ =-0.4145) with gene expression level, followed by CpG island shores (mean spearman rho for both sides' shore ρ=-0.2350). As transcription factors (TFs) have shown to be crucial for AML development, we especially studied differential methylation of an unbiased selection of 1638 TFs. The most enriched differential methylation between CN-AML and normal CD34 positive cells were found in TFs known to be involved in hematopoiesis and with Wilms tumor protein-1 (WT1), activator protein 1 (AP-1) and runt-related transcription factor 1 (RUNX1) being the most differentially methylated TFs. The differential methylation in WT 1 and RUNX1 was located in intragenic regions which were confirmed by pyro-sequencing. AML cases were characterized with respect to mutations in FLT3, NPM1, IDH1, IDH2 and DNMT3A. Correlation analysis between genome wide methylation patterns and mutational status showed statistically significant hypomethylation of CpG Island (p<0.0001) and to a lesser extent CpG island shores (p<0.001) and the presence of DNMT3A mutations. This links DNMT3A mutations for the first time to a hypomethylated phenotype. Further analyses correlating methylation patterns to other clinical data such as clinical outcome are ongoing. In conclusion, our study revealed that non-CpG island regions and in particular enhancers are the most aberrantly methylated genomic regions in AML and that WT 1 and RUNX1 are the most differentially methylated TFs. Furthermore, our data suggests a hypomethylated phenotype in DNMT3A mutated AML. Disclosures: No relevant conflicts of interest to declare.

scholarly journals An analytical pipeline for DNA Methylation Array Biomarker Studies

2021 ◽  
Author(s):  
Jennifer Lu ◽  
Darren Korbie ◽  
Matt Trau
Keyword(s):  
Dna Methylation ◽  
Feature Selection ◽  
Differential Methylation ◽  
The Cancer Genome Atlas ◽  
Methylation Array ◽  
Epigenetic Biomarkers ◽  
Cancer Genome Atlas ◽  
Dna Methylation Array ◽  
Methylation Patterns ◽  
Selection Of

DNA methylation is one of the most commonly studied epigenetic biomarkers, due to its role in disease and development. The Illumina Infinium methylation arrays still remains the most common method to interrogate methylation across the human genome, due to its capabilities of screening over 480, 000 loci simultaneously. As such, initiatives such as The Cancer Genome Atlas (TCGA) have utilized this technology to examine the methylation profile of over 20,000 cancer samples. There is a growing body of methods for pre-processing, normalisation and analysis of array-based DNA methylation data. However, the shape and sampling distribution of probe-wise methylation that could influence the way data should be examined was rarely discussed. Therefore, this article introduces a pipeline that predicts the shape and distribution of normalised methylation patterns prior to selection of the most optimal inferential statistics screen for differential methylation. Additionally, we put forward an alternative pipeline, which employed feature selection, and demonstrate its ability to select for biomarkers with outstanding differences in methylation, which does not require the predetermination of the shape or distribution of the data of interest. Availability: The Distribution test and the feature selection pipelines are available for download at: https://github.com/uqjlu8/DistributionTest Keywords: DNA methylation, Biomarkers, Cancers, Data Distribution, TCGA, 450K

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