The researchers found evidence of four major phenotypes of RA synovium: lymphoid, myeloid, low inflammatory and fibroid, and each type has a distinct underlying gene expression signature. Baseline synovial myeloid, but not lymphoid, gene signature expression was higher in patients with good compared with poor EULAR clinical response to anti-TNFα therapy at Week 16. High baseline serum soluble intercellular adhesion molecule 1 (sICAM1), associated with the myeloid phenotype, and high serum C-X-C motif chemokine 13 (CXCL13), associated with the lymphoid phenotype, had different responses to anti-TNFα therapy (adalimumab) compared with responses to anti-IL6R therapy (tocilizumab). Patients with high sICAM1 and low CXCL13 had the highest ACR50 response rate at Week 24 to anti-TNFα monotherapy compared with patients with low sICAM1 and high CXCL13.
The researchers said that their data demonstrate that underlying molecular and cellular heterogeneity in RA affects clinical outcome to therapy. Patients with the myeloid phenotype exhibited the most robust response to anti-TNFα therapy in their study, suggesting a future pathway to identify and validate serum biomarkers that could predict response to targeted therapies, they concluded.
Timothy B. Niewold, MD, associate professor of medicine at Mayo Clinic in Rochester, Minn., presented research (Abstract 2927) at the 2014 ACR/ARHP Annual Meeting in Boston that looked at the association of circulating type I interferon levels and response to biologic therapies. Results of that research found that the increased pretreatment serum IFN-β:IFN-α inhibition ratio was strongly associated with nonresponse to TNFα inhibition by EULAR criteria at 12–14 weeks. IFN-β:IFN-α ratio greater than 1.3 was significantly more likely to have a nonresponse by EULAR criteria at 12 weeks, and no patient in the study with that ratio or greater achieved a good response. The conclusion was that the blood test may be useful in making treatment decisions about use of TNFα inhibitors in RA.
DAS scores simply measure current disease activity, & we need biomarkers that can predict response to therapy.
Dr. Ruderman describes that type of research as a fine example of “out-of-the-box” thinking that seems wise in the search for biomarkers. The researchers looked at interferon levels, interferon alpha and beta, “and it turns out the ratio of the two was helpful in predicting response to TNF inhibitor therapy, which you wouldn’t think of because it has nothing to do with TNF inhibitor therapy. Obviously the results have to be vetted, and it has to be replicated prospectively, but it was particularly useful at deciding who was unlikely to have a good response to a TNF inhibitor.”
