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Enregistrement W4411417568 · doi:10.1016/j.ard.2025.05.243

OP0232 CANCER INCIDENCE AMONG RHEUMATOID ARTHRITIS PATIENTS TREATED WITH JAK-INHIBITORS COMPARED TO bDMARDs: DATA FROM AN INTERNATIONAL COLLABORATION OF REGISTERS (THE “JAK-POT” STUDY)

2025· article· en· W4411417568 sur OpenAlexaffabout
R. Aymon, Denis Mongin, Benoît Gilbert, Romain Guemara, D. Choquette, Cătălin Codreanu, Louis Coupal, Irini Flouri, Ruth Fritsch‐Stork, R. Giacomelli, D. Huschek, Florenzo Iannone, Tore K Kvien, L. Otero-Varela, Dan Nordström, Karel Pavelká, Manuel Pombo‐Suárez, Sella Aarrestad Provan, Z. Rotar, P. Sidiropoulos, E. Vieira-Sousa, A. Strangfeld, Jakub Závada, Delphine S. Courvoisier, A. Finckh, K. Lauper

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueRheumatoid Arthritis Research and Therapies
Établissements canadiensMontreal Clinical Research Institute
Organismes subventionnairesnon disponible
Mots-clésMedicineRheumatoid arthritisIncidence (geometry)TofacitinibInternal medicineBiologic AgentsOncologyArthritis

Résumé

récupéré en direct d'OpenAlex

Background: Regulatory authorities issued precautionary recommendations on the use of Janus kinase inhibitors (JAKi) following the ORAL Surveillance trial [1], which demonstrated an elevated risk of cancer with tofacitinib, compared to TNF inhibitors (TNFi). While these findings have influenced clinical guidance, there is still limited real-world evidence on malignancy risks associated with JAKi treatment. Objectives: To assess the incidence of cancer in rheumatoid arthritis (RA) patients treated with JAKi, compared to other biologic disease modifying anti-rheumatic drugs (bDMARDs), using data from a large, multi-country, real-world population. Methods: We studied patients from 13 RA registers across Europe and Québec, starting JAKi, TNFi -inhibitors (TNFi) or bDMARDs with other modes of action (OMA). Outcomes of interest were categorized into cancer excluding non-melanoma skin cancer (NMSC); and NMSC cases. Cancers were linked to treatments within 5 years of cessation or until follow-up loss, death, or study end, whichever came first. Incidence rates (IR) per 100 patient-years (PY) with 95% confidence intervals (CI) were computed. Poisson regression, with propensity score weighting (including country, disease-, and patient-characteristics, and comorbidities, see Figure 1), was used to obtain adjusted incidence rate ratios (aIRR), with 95% CI. A sub-analysis was performed on patients aged ≥ 50 years and ≥ 1 cardiovascular risk factor, mimicking the "ORAL Surveillance" trial inclusion criteria (high risk cohort). Results: Over the 53'169 treatment initiations considered in 33'127 patients, 638 cancers excluding NMSC and 219 NMSC were reported. Crude incidence of cancer excluding NMSC was lower for TNFi (2.2/1000 PY) than for JAKi (2.9/1000 PY) and OMA (3.1/1000 PY). The adjusted Poisson regression found no significant difference in the incidence of cancer excluding NMSC (aIRR = 1.10; 95% CI [0.89; 1.37]) or NMSC (aIRR = 1.12; 95% CI [0.78; 1.60]) between JAKi and TNFi, nor between JAKi and OMA (aIRR = 1.07; 95% CI [0.86; 1.32] and aIRR=0.79; 95% CI [0.54; 1.15] respectively). The high risk cohort accounted for 39.4% of treatment courses and had a higher incidence of cancer in each treatment group (OMA: 4.1/1000 PY, JAKi: 4.2/1000 PY, TNFi: 3.2/1000 PY). Similarly to the overall population, no significant difference in the incidence of cancer excluding NMSC (aIRR = 1.16; 95% CI [0.86; 1.57]) and NMSC (aIRR = 1.15; 95% CI [0.71; 1.84]) was observed between JAKi and TNFi, nor between JAKi and OMA (aIRR = 1.09; 95% CI [0.81; 1.47] and aIRR=0.92; 95% CI [0.58; 1.46] respectively). Conclusion: In this real-world study, including 13 RA registers and all currently available JAKi, we did not find a significantly higher risk of cancer excluding NMSC or of NMSC in RA patients treated with JAKi compared to bDMARDs (TNFi or OMA). Further analyses are planned, including the inclusion of additional registries to enhance statistical power and the evaluation of incidence across different exposure periods. REFERENCES: [1] DOI:10.1056/NEJMoa2109927 Figure 1 Table 1Baseline characteristicsJAKitofacitinib (29%)baricitinib (38%)upadacitinib (26%)filgotinib (7%)n = 13'945OMArituximab (30%)tocilizumab (32%)abatacept (24%)sarilumab (9%)other (5%)n = 15'552TNFietanercept (34%)adalimumab (35%)golimumab (9%)certolizumab (9%)infliximab (5%)unspecified (8%)n = 23'672Treatment duration, years (median [IQR])1.6 [0.6; 3.1]1.0 [0.8; 2.1]1.4 [0.5; 3.1]Age, years (mean (SD))57.6 (12.2)60.2 (12.5)56.3 (13.3)Female (%)81.478.578.7Disease duration, years (median [IQR])10.4 [5.0, 17.5]10.6 [5.2, 17.8]7.5 [3.2, 14.0]Seropositivity (%)78.882.875.1Previous b/ts DMARD (%)023.116.135.9121.825.333.7220.220.415.4≥ 334.838.114.9Concomitant csDMARD (%)51.054.565.8Concomitant GC (%)45.547.040.8CRP, mg/L (mean (SD))11.2 (20.8)12.6 (27.9)11.6 (21.8)CDAI (mean (SD))27.1 (13.5)24.6 (13.8)25.8 (13.6)DAS28 (mean (SD))4.5 (1.6)4.0 (1.8)4.5 (1.6)HAQ (mean (SD))1.2 (0.7)1.2 (0.8)1.1 (0.7)BMI (mean (SD))26.8 (5.6)26.9 (5.5)27.0 (5.6)Tobacco (ever) (%)33.134.032.8Past malignancy (%)3.77.03.6JAKi = Janus kinase inhibitors, OMA=other mode of action bDMARDs, TNFi=TNF inhibitors, IQR=interquartile range, SD=standard deviation, csDMARDs=conventional synthetic DMARDs, GC=glucocorticoids, CRP=C-reactive protein, CDAI=Clinical Disease Activity Index, DAS28=Disease Activity Score 28, HAQ=Health Assessment Questionnaire, BMI=Body Mass Index. Acknowledgements: This study is investigator initiated. The JAK-pot collaboration is supported by unconditional/unrestricted research grants from AbbVie Inc., Eli Lilly and Co., and Alfasigma S.p.A. and was previously supported by Pfizer Inc and Galapagos NV. Disclosure of Interests: Romain Aymon: None declared, Denis Mongin: None declared, Benoit Gilbert: None declared, Romain Guemara: None declared, Denis Choquette: None declared, Catalin Codreanu: None declared, Louis Coupal: None declared, Irini Flouri: None declared, Ruth Fritsch-Stork: None declared, Roberto Giacomelli: None declared, Doreen Huschek: None declared, Florenzo Iannone Abbvie, Galapagos, Eli-Lilly, Pfizer, UCB, Abbvie, Janssen, UCB, Galapagos, Tore K. Kvien Grünenthal, Janssen, Sandoz, AbbVie, Gilead, Janssen, Novartis, Pfizer, Sandoz, UCB, AbbVie,BMS, Galapagos, Novartis, Pfizer, UCB, Lucía Otero-Varela: None declared, Dan Nordström MSD, Novartis, Pfizer, UCB, Karel Pavelka AbbVie, Eli Lilly, Sandoz, UCB, Medac, Pfizer, Manuel Pombo-Suarez: None declared, Sella Aarrestad Provan: None declared, Ziga Rotar Abbvie, Amgen, AstraZeneca, Boehringer, Biogen, Eli Lilly, Janssen, Medis, MSD, Novartis, Pfizer, Sandoz Lek, Stada, SOBI, Abbvie, AstraZeneca, Boehringer, Eli Lilly, Janssen, Medis, MSD, Novartis, Pfizer, Sandoz Lek, SOBI, Prodromos Sidiropoulos: None declared, Elsa Vieira-Sousa: None declared, Anja Strangfeld AbbVie, Galapagos, Lilly, Pfizer, Takeda, UCB, Unconditional grant to my institution for the RABBIT register with equal parts from AbbVie, Amgen, BMS, Celltrion, Fresenius Kabi, Galapagos, Hexal, Lilly, MSD, Viatris, Pfizer, Roche, Samsung Bioepis, Sanofi-Aventis, and UCB, Jakub Závada Abbvie, Elli-Lilly, Sandoz, Novartis, Egis, UCB, Sanofi, AstraZeneca, Sobi, Abbvie, Novartis, AstraZeneca, Glaxo, Delphine Sophie Courvoisier: None declared, Axel Finckh AbbVie, Astra Zeneca, Eli-Lilly, Pfizer, UCB, AbbVie, Alfasigma, Eli-Lilly, Galapagos, Pfizer, Kim Lauper Pfizer. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,212
Score d'incertitude au seuil0,422

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,004
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,037
Tête enseignante GPT0,357
Écart entre enseignants0,320 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2025
Routes d'admission2
Résumé présentoui

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