1038: Genetic and Molecular Distinctions Between Axial Psoriatic Arthritis and Ankylosing Spondylitis

Arthur Kavanaugh¹, Xenofon Baraliakos², Sheng Gao³, Warner Chen³, Kristen Sweet³, Soumya Chakravarty⁴, Qingxuan Song³, May Shawi⁵, Frank Behrens⁶ and Proton Rahman⁷, ¹University of California San Diego, San Diego, CA, USA, San Diego, CA, ²Rheumazentrum Ruhrgebiet Herne, Herne, Germany, ³Janssen Research and Development, LLC, Spring House, PA, ⁴Janssen Scientific Affairs, LLC; Drexel University College of Medicine, Villanova, PA, ⁵Immunology Global Medical Affairs, Janssen Pharmaceutical Companies of Johnson & Johnson, Horsham, PA, ⁶Rheumatology University Hospital & Fraunhofer Institute Translational Medicine and Pharmacology, Goethe-University Frankfurt, Frankfurt Am Main, Germany, ⁷Memorial University, St. John's, NL, Canada

Background/Purpose: Despite overlapping symptoms, axial psoriatic arthritis (axPsA) and ankylosing spondylitis (AS) may be distinct disorders with differing clinical manifestations, genetic associations, and radiographic findings.¹ While guselkumab (GUS), a human monoclonal antibody targeting the interleukin (IL)-23p19 subunit, improved symptoms of axPsA,² risankizumab, a humanized monoclonal antibody targeting the IL-23p19 subunit, did not show improvement in the primary endpoint of proportion of AS patients (pts) achieving an ASAS40 response at week (W) 12.³ A better understanding of the molecular distinctions between axPsA and AS is needed to differentiate these diseases and guide treatment choice.

Methods: Whole blood and serum samples were collected from consenting pts in the NCT03162796/NCT0315828 studies of GUS in PsA and the NCT02437162/NCT02438787 studies of ustekinumab in AS. Human leukocyte antigen (HLA) genotypes were determined by RNA sequencing, limited to Caucasian pts to reduce genetic variability,⁴ and select serum cytokine levels were analyzed alongside samples from healthy individuals. Differential prevalence of HLA alleles in axPsA vs AS was determined using a Fisher's Exact test. Statistical significance of differential baseline (BL) serum cytokine expression among axPsA vs non-axPsA vs AS pts, and of GUS effect on serum cytokine reduction vs placebo among axPsA and non-axPsA pts, were determined with a generalized linear model performed on log2-transformed data. Biomarker data from GUS every-4-weeks and every-8-weeks treatment arms were pooled.

Results: Among 186/234 Caucasian axPsA/AS pts with available data, 34%/15% were female, 70%/14% used methotrexate at BL, mean serum CRP levels were 2.8/2.4 mg/dL, and mean BASDAI scores were 6.4/7.5, respectively. Aside from race, BL demographics and disease characteristics were representative of the overall population. The prevalence of class I HLA allele -B27, -C01, and -C02 carriers was significantly lower in axPsA than AS pts (30.7% vs 92.3%, p< 0.001; 5.9% vs 31.6%, p< 0.001; and 28.0% vs 62.0%, p< 0.001, respectively), while the prevalence of HLA-C06 was significantly higher in axPsA than AS populations (36.0% vs 8.6%, p< 0.001). BL serum levels of IL-17A and IL-17F were significantly higher in axPsA (N=71) than in AS (N=58) pts (p< 0.01 and p< 0.001, respectively). Comparable IL-17A/IL-17F expression was seen for axPsA and non-axPsA (N=229) pts (both p=NS). Significant and comparable reductions from BL in serum IL-17A/IL-17F in axPsA and non-axPsA pts were seen with GUS treatment (axPsA N=41, non-axPsA N=160) vs placebo (axPsA N=30, non-axPsA N=69) at W4/24 (all p< 0.05).

Conclusion: Adults with axPsA and AS exhibit different genetic risk factors and serum IL-17 levels, supporting the concept of distinct disorders. GUS demonstrated significant pharmacodynamic effects in axPsA pts that aligned with such effects in non-axPsA pts, consistent with observed clinical improvement.²

References:
1. Feld et al. Nat Rev Rheumatol. 2018;14(6):363-371.
2. Mease et al. Lancet Rheumatol. 2021;3(10)E715-E723.
3. Baeten et al. Ann Rheum Dis. 2018;77(9):1295-1302.
⁴Buchkovich et al. Genome Med. 2017;9(86).

Disclosures: A. Kavanaugh, Abbvie, Amgen, Bristol-Myers Squibb(BMS), Eli Lilly, Genentech, Janssen, Merck, Novartis, Pfizer, UCB; X. Baraliakos, AbbVie, Lilly, Galapagos, MSD, Novartis, Pfizer, UCB, Bristol-Myers Squibb, Janssen, Roche, Sandoz, Sanofi; S. Gao, Janssen Research & Development, LLC; W. Chen, Janssen Research & Development, LLC, Johnson & Johnson; K. Sweet, Janssen Research & Development, LLC, Johnson & Johnson; S. Chakravarty, Janssen Scientific Affairs, LLC, Johnson & Johnson; Q. Song, Janssen Research & Development, LLC, Johnson & Johnson; M. Shawi, Janssen Pharmaceutical Companies of Johnson and Johnson; F. Behrens, AbbVie, Boehringer Ingelheim, Celgene, Chugai, Eli Lilly, Genzyme, Janssen, MSD, Novartis, Pfizer, Roche, Sanofi, Bristol-Myers Squibb(BMS), Galapagos, Gilead, UCB, Affibody, MoonLake, GlaxoSmithKlein(GSK); P. Rahman, Janssen, Novartis, AbbVie, Eli Lilly and Company, Pfizer.