ObjectivesTo compare the safety and effectiveness of biologic and conventional disease-modifying antirheumatic drugs (DMARDs) for immune checkpoint inhibitor-associated inflammatory arthritis (ICI-IA).MethodsThe retrospective multicentre observational study included patients with a diagnosis of ICI-IA treated with a tumour necrosis factor inhibitor (TNFi), interleukin-6 receptor inhibitor (IL6Ri) and/or methotrexate (MTX); patients with pre-existing autoimmune disease were excluded. The primary outcome was time to cancer progression from ICI initiation; the secondary outcome was time to arthritis control from DMARD initiation. Cox proportional hazard models were used to compare medication groups, adjusting for confounders.Results147 patients were included (mean age 60.3 (SD 11.9) years, 66 (45%) women). ICI-IA treatment was TNFi in 33 (22%), IL6Ri 42 (29%) and MTX 72 (49%). After adjustment for time from ICI initiation to DMARD initiation, time to cancer progression was significantly shorter for TNFi compared with MTX (HR 3.27 (95% CI 1.21 to 8.84, p=0.019)) while the result for IL6Ri was HR 2.37 (95% CI 0.94 to 5.98, p=0.055). Time to arthritis control was faster for TNFi compared with MTX (HR 1.91 (95% CI 1.06 to 3.45, p=0.032)) while the result for IL6Ri was HR 1.66 (95% CI 0.93 to 2.97, p=0.089). A subset analysis in patients with melanoma gave similar results for both cancer progression and arthritis control.ConclusionThe treatment of ICI-IA with a biologic DMARD is associated with more rapid arthritis control than with MTX, but may be associated with a shorter time to cancer progression.

Immune checkpoint inhibitor (ICI) therapies used to treat cancer, such as anti–PD-1 antibodies, can induce autoimmune conditions in some individuals. The T cell mechanisms mediating such iatrogenic autoimmunity and their overlap with spontaneous autoimmune diseases remain unclear. Here, we compared T cells from the joints of 20 patients with an inflammatory arthritis induced by ICI therapy (ICI-arthritis) with two archetypal autoimmune arthritides, rheumatoid arthritis (RA) and psoriatic arthritis (PsA). Single-cell transcriptomic and antigen receptor repertoire analyses highlighted clonal expansion of an activated effector CD8 T cell population in the joints and blood of patients with ICI-arthritis. These cells were identified as CD38 hi CD127 − CD8 T cells and were uniquely enriched in ICI-arthritis joints compared with RA and PsA and also displayed an elevated interferon signature. In vitro, type I interferon induced CD8 T cells to acquire the ICI-associated CD38 hi phenotype and enhanced cytotoxic function. In a cohort of patients with advanced melanoma, ICI therapy markedly expanded circulating CD38 hi CD127 − T cells, which were frequently bound by the therapeutic anti–PD-1 drug. In patients with ICI-arthritis, drug-bound CD8 T cells in circulation showed marked clonal overlap with drug-bound CD8 T cells from synovial fluid. These results suggest that ICI therapy directly targets CD8 T cells in patients who develop ICI-arthritis and induces an autoimmune pathology that is distinct from prototypical spontaneous autoimmune arthritides.

BackgroundImmune checkpoint inhibitors (ICI) can potentially cause ICI-inflammatory arthritis (ICI-IA), which often resembles rheumatoid arthritis (RA). In this study, we examined the degree of anticitrullinated peptide antibodies (ACPA) epitope expansion in CCP+ICI-IA and patients with RA.MethodsWe used clinical data and serum from ICI-IA and patients with RA with early disease as well as longstanding disease. A custom, bead-based antigen array was used to identify IgG ACPA reactivities to 18 putative RA-associated citrullinated proteins. Hierarchical clustering software was used to create a heatmap to identify ACPA levels. Additionally, HLA DRB1 typing was performed on ICI-IA patients as well as controls of patients treated with ICI that did not develop ICI-IA (ICI controls).ResultsCompared to patients with CCP+RA, patients with CCP+ICI-IA were older (p<0.001), less likely to have positive rheumatoid factor (p<0.001) and had a shorter duration of symptoms (p<0.001). There were less ACPA levels and a lower number of distinct ACPA epitopes in the serum of patients with ICI-IA compared with longstanding patients with RA (p<0.001). Among those tested for HLA DRB1, there were no differences in the frequency of the shared epitope between those with ICI-IA and ICI controls.ConclusionPatients with ICI-IA had lower ACPA titres and targeted fewer ACPA epitopes than longstanding patients with RA, and there were no significant differences in the presence of the shared epitope between those that developed ICI-IA and ICI controls. It remains to be determined if ICI-IA represents an accelerated model of RA pathogenesis with ICI triggering a transition from preclinical to clinical disease.

BackgroundImmune checkpoint inhibitor associated arthritis (ICI-A) commonly persists for months to years, even after ICI cessation.[1]ObjectivesTo compare the safety and effectiveness of biologic and conventional disease modifying anti-rheumatic drugs (DMARDs) for ICI-A.MethodsRetrospective multicenter observational study. Inclusion: 1) diagnosis of ICI-A and 2) treatment with a tumor necrosis factor inhibitor (TNFi), interleukin-6 receptor inhibitor (IL6Ri) and/or methotrexate (MTX). Exclusion: preexisting autoimmune disease. The primary outcome was time to cancer progression from ICI initiation. Patients whose cancer progressed prior to DMARD initiation were excluded from this analysis. The secondary outcome was time to arthritis control from DMARD initiation, defined as grade 1 arthritis and prednisone ≤10mg/day. Cox proportional hazard models were generated, adjusting for confounders. A sensitivity analysis was performed incorporating a time dependent variable “time from ICI initiation to DMARD initiation.”Results147 patients were included, mean (SD) age 60.3 (11.9) years, 66 (45%) females. Sixty percent had received PD1/PDL1 monotherapy, 30% received combination CTLA4/PD1. Eighty percent had stage IV cancer. ICI-A treatment was TNFi in 33 (22%), IL6Ri 42 (29%), MTX 72 (49%) (Table 1). A Kaplan-Meier curve showing time to cancer progression by DMARD is shown inFigure 1. In an unadjusted Cox model with MTX as the reference, time to cancer progression with a TNFi was HR 2.51 (95% CI 0.91-6.93, p=0.075) and for IL6Ri HR 2.36 (95% CI 0.91-6.12, p=0.078). After adjustment for the time dependent variable, time to cancer progression was significantly shorter for TNFi-treated patients compared to MTX, HR 3.27 (95% CI 1.21-8.84, p=0.019). The result for IL6Ri was HR 2.31 (95% CI 0.98-5.41, p=0.055). Time to arthritis control was significantly faster for TNFi compared to MTX, HR 1.91 (95% CI 1.06-3.45, p=0.032) in an adjusted Cox model. Results for IL6Ri were HR 1.66 (95% CI 0.93-2.97, p=0.089). Results for cancer progression and arthritis control were similar in the subset of patients with melanoma.ConclusionTreatment of ICI-A with biologic DMARDs is associated with more rapid arthritis control than with MTX but may be associated with a shorter time to cancer progression. A prospective randomized controlled trial is needed to verify these findings and to identify the optimal approach to managing patients with high grade ICI-A.Reference[1] Braaten TJ, et al Ann Rheum Dis. 2020 Mar;79(3):332-338.Table 1.Patient characteristicsTotalTNFiIL6RMTXp-valueN147 (100%)33 (22%)42 (29%)72 (49%)Age, mean (SD)60.3 (11.9)56.3 (14.0)61.5 (12.5)61.5 (10.1)0.085Sex (female)66 (45%)13 (39%)15 (36%)38 (53%)0.17Race (white)136 (92%)30 (91%)40 (95%)66 (92%)0.18Cancer type0.068Melanoma63 (43%)16 (48%)21 (50%)26 (36%)Non-small cell lung cancer15 (10%)1 (3%)0 (0%)14 (19%)Renal cell carcinoma24 (16%)5 (15%)12 (29%)7 (10%)Bladder cancer7 (5%)1 (3%)3 (7%)1 (4%)Other38 (25%)10 (30%)6 (14%)22 (31%)Cancer stage0.34III26 (18%)7 (21%)6 (14%)13 (18%)IV118 (80%)24 (73%)36 (86%)58 (81%)Checkpoint inhibitor0.91PD1/PDL1 monotherapy101 (69%)24 (73%)28 (67%)49 (68%)Combination (CTLA4/PD1)44 (30%)9 (27%)13 (31%)22 (31%)ICI discontinued for arthritis58 (40%)12 (36%)17 (40%)29 (40%)0.92ICI initiation to DMARD start (days), median (IQR)403 (258,638)411 (284, 622)300 (170, 435)486(258, 675)0.020Duration of DMARD treatment, median (IQR)278 (77, 546)92 (45, 149)309 (63, 483)420 (138, 765)<0.001Maximum glucocorticoid dose, mean (SD)40 (27)53 (27)42 (28)33 (23)0.002Figure 1.Kaplan-Meier curve showing time to cancer progression from time of immune checkpoint inhibitor initiationAcknowledgements:NIL.Disclosure of InterestsAnne Bass: None declared, Noha Abdel-Wahab Speakers bureau: ChemoCentryx, Consultant of: ChemoCentryx, Pankti Reid: None declared, Jeffrey Sparks Consultant of: Bristol Myers Squibb, AbbVie, Amgen, Boehringer Ingelheim, Gilead, Inova Diagnostics, Janssen, Optum, Pfizer, Grant/research support from: Bristol Myers Squibb, cassandra calabrese Speakers bureau: Sanofi, Consultant of: Astazenica, Deanna Jannat-Khah: None declared, Nilasha Ghosh: None declared, Divya Rajesh: None declared, Carlos Aude: None declared, Lydia Gedmintas: None declared, Lindsey MacFarlane: None declared, Senada Arabelovic: None declared, Adewunmi Falohun: None declared, Komal Mushtak: None declared, Farah Al Haj: None declared, Adi Diab: None declared, Ami Shah Grant/research support from: Eicos Sciences, Medpace LLC, Arena Pharmaceuticals, Kadmon Corporation, Clifton Bingham Consultant of: Bristol Myers Squibb,: Abbvie, Janssen, Lilly, Pfizer, Sanofi, Moderna, Grant/research support from: Bristol Myers Squibb, Karmela Kim Chan: None declared, Laura Cappelli Consultant of: Bristol Myers Squibb, Grant/research support from: Bristol Myers Squibb.

BackgroundIt is unknown whether particular presentations of immune checkpoint inhibitor-associated inflammatory arthritis (ICI-IA) are associated with better cancer responses or faster time to arthritis control. Machine learning methods have the ability to determine important factors in datasets where there are often many variables.ObjectivesTo identify variables associated with arthritis control and cancer progression among persons with ICI-IA.MethodsThis study is ancillary to a retrospective observational study of 147 DMARD-treated (TNFi, IL-6Ri, or methotrexate) patients with ICI-IA seen at 6 U.S sites. Variables from the medical record included demographics, patterns of swollen joints, medications, lab values, and concomitant irAEs. Arthritis control and cancer progression were the two outcomes analyzed. survival Classification and Regression trees (sCART) were created using the rpart and partykit R packages[1]. Random Survival Forest (RSF) was performed using the R package randomForestSRC[2]. Variable importance (VI) for sCART and Relative Importance Score (RIS) were computed to assess influential variables..ResultsWe analyzed 147 people with ICI-IA. 69% received PD-1/PD-L1 monotherapy, and 43% had melanoma. ICI-A treatment was a TNFi in 17%, IL6Ri in 26%, methotrexate in 33%, and 24% received >1 DMARD. Median time to cancer progression was 333 (IQR 110, 811) days for the 26% that progressed. Median time to arthritis control was 109 days (IQR 32, 287) for the 93% that achieved control.For cancer progression the following were identified by both sCART and RSF as important: steroid duration, total joint count (TJC), study site, maximum steroid dose, ICI type, shoulder arthritis, >1 DMARD and number of IRAEs. For classifying arthritis control, the following variables were found to be important in both sCART and RSF: steroid duration, >1 DMARD, elbow arthritis, age, cancer type, TJC and first DMARD (Table 1). The Figure 1 shows the sCART for arthritis control.ConclusionBoth methods, SCART and RSF, demonstrated the important influence of steroid duration on arthritis control and cancer progression. Machine learning methods demonstrated the potential prognostic importance of specific joint involvement for each outcome- knee for time to arthritis control and shoulder and wrist for cancer progression.References[1]Bou-Hamad I, Larocque D, Ben-Ameur H. A review of survival trees.Stat Surv. 2011;5:44-71.[2]Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests.Ann Appl Stat. 2008;2(3):841-860.Table 1.Demographics, Variable Importance (VIMP) from survival Classification and Regression Trees (sCART) and Relative Importance Score (RIS) from Random Survival Forest (RSF).Arthritis ControlCancer ProgressionCharacteristic(%) Or median(IQR)sCART VIMP1RSF RIS2sCART VIMP1RSF RIS2Steroid duration, days318 (181, 658)121.0231Cancer type60.4914First DMARD class30.372>1 DMARD36 (24.5)110.3340.37ICI type*10.0550.22Study site190.0280.36Age (years)62.3 (52.2, 69.0)70.42100.04Maximum prednisone dose (mg)40 (20,60)460.10Arthritis affected JointsKnee106 (72.1)10.75Shoulder54 (36.7)1120.49Wrist82 (55.8)0.460.41Hand111 (75.5)0.230.35Elbow44 (29.9)100.251Ankle64 (43.5)40.10Total Joint Count3 (2, 4)50.23150.26Characteristics ≤3 for sCART VIMP and ≤0.3 RIS from RSFSex, cancer stage, number of IRAEs, Sicca, thyroiditis, colitis, RF+, RF+ or CCP+, arthritis affecting hip, feet1sCART VIMP the higher the number the more influential the variable2sRF RIS A value of 1 indicates an influential variable; values that are negative or close to 0 are “noise” variables.*ICI combination vs monotherapyFigure 1.Survival Classification and Regression Tree for Arthritis Control. Kaplan-Meier curves are displayed at the bottom for each terminal node (or group) with time on the x axis measured in days.Acknowledgements:NIL.Disclosure of InterestsDeanna Jannat-Khah Shareholder of: Dr. Jannat-Khah owns shares of Walgreens Boots Alliance, AstraZeneca, and Cytodyn., Grant/research support from: Dr. Jannat-Khah has a grant from the Hospital for Special Surgery., Laura Cappelli Grant/research support from: Dr. Cappelli has a grant from the NIH (NIAMS K23AR075872) and from Bristol Myers Squibb, Pankti Reid Consultant of: Dr. Reid was a consultant for Level Ex, Grant/research support from: Dr. Reid has grant support from the following: COVID-19 Funds to Retain Clinical Scientists by the Supporting Early Career University Researchers to Excel through Disruptions Steering Committee and The University of Chicago Institute of Translational Medicine Clinical and Translational Science Award K12/KL2 Grant 5KL2TR002387-05, Jeffrey Sparks Consultant of: Dr. Sparks was a consultant for AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Gilead, Inova Diagnostics, Janssen, Optum, and Pfizer., Grant/research support from: Dr. Sparks has received grant support from the following entities: Bristol Myers Squibb, National Institute of Arthritis and Musculoskeletal and Skin Diseases, Rheumatology Research Foundation, R.Bruce and Joan M. Mickey Research Scholar Fund, and Llura Gund Award for Rheumatoid Arthritis Care and Research, Noha Abdel-Wahab Speakers bureau: Dr. Abdel-Wahab received an honorarium for a lecture by ChemoCentryx, Consultant of: Dr. Abdel-Wahab was a consultant for ChemoCentryx, Grant/research support from: Dr. Abdel-Wahab has grant funding from the following institutions: National Institute of Allergy and Infectious Disease (NIH-K01AI163412) and University of Texas MD Anderson, cassandra calabrese Speakers bureau: Dr. Calabrese received an honorarium for a lecture from Sanofi., Consultant of: Dr. Calabrese was a consultant for Lilly, and AstraZeneca, Carlos Aude: None declared, Nilasha Ghosh Grant/research support from: Dr. Ghosh has a grant from the Hospital for Special Surgery, Karmela Kim Chan: None declared, Anne Bass Grant/research support from: Dr. Bass has grants from the following institutions: Hospital for Special Surgery, Memorial Sloane Kettering Cancer Center and Rheumatology Research Foundation.

BackgroundImmune checkpoint inhibitor (ICI) therapy has greatly improved the prognosis of advanced cancers but is often complicated by immune-related adverse events (irAEs). Biomarkers of ICI safety and efficacy are needed.ObjectivesTo identify autoantibodies (AAbs) associated with irAEs and cancer outcomes in ICI-treated patients.MethodsThis analysis used clinical data and biospecimens from a prospective trial of 60 patients with advanced melanoma who received 2-4 doses of combination ICI (anti-PD1 + anti-CTLA4) followed by anti-PD1 monotherapy.[1]Patients were followed for three years for irAE development, cancer progression, and death. Plasma was collected at baseline and six weeks after ICI initiation and assayed for AAbs using PhIP-Seq with a human peptide library spanning the entire human proteome. Baseline levels of AAbs with a ≥2 fold-change in ≥10 patients at follow-up (n = 833 AAbs) were concurrently included in a bootstrapped LASSO Cox proportional hazards model (1000 replications; α = 0.5; λ chosen by cross-validation for each replication) that was separately run for each outcome: ≥1 severe irAE (CTCAE grade ≥ 3), progression-free survival (PFS), and overall survival (OS). The proportion of times each AAb was replicated was reported as its “validation frequency.”[2]Hazard ratio confidence intervals were reported after individually calculating bias-corrected and accelerated (BCa) bootstrap intervals (1000 replications) for each AAb. The AAbs with the highest validation frequencies were used for Partial Least Squares Discriminant Analysis (PLS-DA) clustering.ResultsAmong the 60 patients, 47% developed a severe irAE, 25% progressed, and 23% died.Table 1shows the six AAbs with the highest validation frequencies for severe irAE, PFS, and OS. PLS-DA was able to cluster patients by the development of severe irAEs using only six AAbs with a 10-fold cross-validated AUROC of 0.86 ± 0.01 (p <.001) (Figure 1). PLS-DA performed similarly well for PFS and OS.Table 1reflects that higher levels of the identified AAbs at baseline were associated with worse PFS and OS (HR > 1); this pattern remained true across the 25 AAbs with the highest validation frequencies for PFS (21/25, 84%) and OS (23/25, 92%). Conversely, amongst the top AAbs linked to severe irAEs, lower levels in the majority (15/25, 60%) of the identified AAbs were associated with this outcome (HR < 1). Most of the identified top AAbs were unique to each outcome, but there was some overlap: PFS and OS shared seven AAbs (28%), PFS and severe irAEs shared two AAbs (8%), and OS and severe irAEs shared one AAb (4%). No AAbs overlapped between more than two outcomes.ConclusionThese results suggest that specific autoantibodies may be useful biomarkers for predicting outcomes in melanoma patients receiving ICI therapy. However, these results require validation.References[1]Postow MA, et al. Journal of Clinical Oncology 2022;40:1059-1067[2]Yang JJ, et al. Blood 2012;120:4197-4204Table 1.Hazard ratios and 95% confidence intervals for the top six autoantibodies for each outcome≥1 Severe irAE (n = 60, events = 28)Progression-Free Survival (PFS) (n = 60, events = 15)Overall Survival (OS) (n = 60, events = 14)AAbHazard Ratio (95% CI)VF*sVF**AAbHazard Ratio (95% CI)VFsVFAAbHazard Ratio (95% CI)VFsVFAKAP91.25(1.08-1.42)76.4%100ACAN1.72(1.21-2.35)66.3%100KHSRP1.34(1.13-1.65)52.8%100KTN11.26(1.02-1.50)60.0%79POLR2A1.29(1.04-1.57)62.7%95EN21.31(1.05-1.61)51.8%98GCN10.66(0.45-0.95)54.4%78ZGRF11.47(1.03-1.81)50.9%77SBNO21.33(1.04-1.65)50.0%95FCHO21.21(0.98-1.48)49.5%71DYNC2H11.39(0.97-1.85)45.2%68PAK41.46(1.16-1.87)43.5%82NRXN20.72(0.44-0.95)44.2%58TRIOBP1.26(1.05-1.43)41.3%62DYNC2H11.35(0.95-1.78)39.0%74EPC11.28(1.09-1.53)43.2%57SDCCAG81.40(1.08-1.74)39.4%59ACAN1.43(0.71-2.11)34.0%64*VF = Validation Frequency (proportion of times each AAb was replicated by the LASSO model)**sVF = Scaled Validation Frequency (validation frequency scaled to highest value for each outcome)Acknowledgements:NIL.Disclosure of InterestsCarlos Aude: None declared, Nilasha Ghosh Grant/research support from: Hospital for Special Surgery, Deanna Jannat-Khah Shareholder of: Walgreens Boots Alliance, AstraZeneca, and Cytodyn, Grant/research support from: Hospital for Special Surgery, Karmela Kim Chan: None declared, Michael Postow Consultant of: BMS, Merck, Novartis, Eisai, Pfizer, and Chugai, Grant/research support from: RGenix, Infinity, BMS, Merck, and Novartis, H Larman Consultant of: Scientific Advisory Board member for TScan Therapeutics, Employee of: Founder of ImmuneID, Portal Bioscience, and Alchemab, Anne Bass Grant/research support from: Hospital for Special Surgery, Memorial Sloan Kettering Cancer Center, and the Rheumatology Research Foundation.

BackgroundThe use of immune checkpoint inhibitors (ICIs) for the treatment of cancer can be associated with adverse events including inflammatory arthritis (ICI-IA). ICI-IA can encompass a variety of different phenotypes including: polymyalgia rheumatica (PMR)-like, spondyloarthritis-like, rheumatoid arthritis (RA)-like, and others[1]. Due to the heterogeneous clinical presentation of ICI-IA, methods are needed to find important variables and associations to describe and distinguish between clinical phenotypes.ObjectivesUse supervised machine learning techniques to find important variables associated with classification of arthritis phenotypes among patients with ICI-IA.MethodsThis study is ancillary to a retrospective observational study of patients with ICI-IA at 6 U.S sites who were treated with DMARDS (TNFi, IL6R, or methotrexate). Arthritis phenotypes was the classification target of interest. Demographics, patterns of swollen joints, medications, lab values, and concomitant irAEs were all obtained from the medical record. Descriptive statistics were computed. Classification and Regression Trees (CART) were created using the rpart and partykit packages and Random Forest (RdF) using the randomForest R package. One thousand trees were created for the forest. Variable importance scores (CART) and mean decrease in Gini scores (RdF) were created to assess the variable importance of included variables.Results147 patients were included, mean age 62 years, 43% with melanoma. Arthritis phenotypes were PMR-like in 8.3%, spondylarthritis-like 19%, RA-like 48%, polyarthritis 17%, and other 8%. Two versions of the CART and RdF were performed (with and without study site) (Table 1). Including study site, the following were identified by both CART and RdF as important: study site was the most important variable associated with the phenotypic label, followed by cancer type, hand arthritis, age, total joint count (TJC). The CART flow diagram with study site is shown in theFigure 1. Excluding study site, CART and RdF identified hand arthritis, cancer type, age, TJC.ConclusionWe found that classification of arthritis phenotype was study site-specific in our statistical models. After excluding site, age, cancer type, TJC, maximum steroid dose, and hand arthritis were associated with phenotypic classification. The influence of study site illustrates current heterogeneity in ICI-IA phenotyping among experts and the need for formalized classification for enrollment in clinical trials. These results identify variables that could be explored by the ACR/EULAR ICI-IA classification criteria working group.Reference[1]Ghosh N., Bass A. Rheumatic Complications of Immune Checkpoint Inhibitors. Med. Clin. North Am. 2021 Mar; 105(2):227-245.Table 1.Variable Importance (VI) from Classification and Regression Tree (CART) and mean decrease in Gini score (MDG) from Random forest (RdF) associated with Immune Checkpoint Inhibitor Inflammatory Arthritis phenotype assignment.CharacteristicCART VI1RdF MDG2CART VI1without study siteRdF MDG2without study siteStudy site2510.8Not includedAge1210.61312.2Cancer type166.7207.7Maximum steroid dose of prednisone (mg)7.38.7Total Joint Count45.966.2Hand arthritis143.7254.0Factors with MDG <=3cancer stage, rash, Number of IRAEs, sex, Rheumatoid factor positive(RF+), RF+ or cyclic citrullinated peptide factor positive, arthritis affecting: shoulder, knee, ankle, hip, wrist, and knee1CART Variable importance, the higher the number the more influential the variable is in the CART2Mean Decrease in Gini score from RdF is a measure to rank variables; the higher the number the more important the variable.Figure 1.Classification and Regression Tree for Arthritis Phenotype with Study Site included. The bar graphs at the bottom show which arthritis phenotype is the majority for each terminal node and the y-axis shows the error rates for each group from the model.Acknowledgements:NIL.Disclosure of InterestsDeanna Jannat-Khah Shareholder of: Dr. Jannat-Khah owns shares of Walgreens/Boots Alliance, AstraZeneca, and Cytodyn (non Rheumatologic pharmaceutical company), Grant/research support from: Dr. Jannat-Khah has a grant from Hospital for Special Surgery for research., Nilasha Ghosh Grant/research support from: Dr. Ghosh has a grant from Hospital for Special Surgery for research., Laura Cappelli Grant/research support from: Dr. Cappelli has research grants from the NIH (NIAMS K23AR075872) and from Bristol-Myers Squibb., Pankti Reid Consultant of: Dr. Reid was a consultant for Level Ex, Grant/research support from: Dr. Reid has grant support from the following: COVID-19 Funds to Retain Clinical Scientists by the Supporting Early Career University Researchers to Excel through Disruptions Steering Committee and The University of Chicago Institute of Translational Medicine Clinical and Translational Science Award K12/KL2 Grant 5KL2TR002387-05, Jeffrey Sparks Consultant of: Dr. Sparks was a consultant for AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Gilead, Inova Diagnostics, Janssen, Optum, and Pfizer, Grant/research support from: Dr. Sparks has received grants from the following entities: Bristol Myers Squibb, National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIH), Rheumatology Research Foundation, R.Bruce and Joan M. Mickey Research Scholar Fund, Llura Gund Award for Rheumatoid Arthritis Care and Research., Noha Abdel-Wahab Speakers bureau: Dr. Abdel-Wahab was a speaker for ChemoCentryx., Consultant of: Dr. Abdel-Wahab was a consultant for ChemoCentryx., Grant/research support from: Dr Abdel-Wahab has grant funding from the NIAD (K01AI163412) and from the University of Texas MD Anderson, cassandra calabrese Paid instructor for: Dr. Calabrese received an honorarium for a lecture by Sanofi, Consultant of: Dr. Calabrese was a consultant for Lilly, and AstraZeneca., Carlos Aude: None declared, Karmela Kim Chan: None declared, Anne Bass Grant/research support from: Dr Bass has grants from the Hospital for Special Surgery, Memorial Sloane Kettering Cancer Center, and the Rheumatology Research Foundation.