Potential Predictor of Treatment Response in Rheumatoid Arthritis Patients Identified

Researchers able to predict treatment response to TNFI drugs and rituximab in RA patients.

In rheumatoid arthritis (RA) patients, the efficacy of tumor necrosis factor inhibitors (TNFi) and B cell depletion therapies can sometimes be hit or miss.

New research, however, indicates that 3 gene expression signatures can help rheumatologists better predict the likelihood of response to these treatments.

The researchers used data from the randomized, controlled ORBIT study, which examined RA patients in the United Kingdom, to search for gene expression markers that could help predict responses to either TNFi drugs, the B cell therapy rituximab, or both.

“[The ORBIT data] showed that patients who have seropositive rheumatoid arthritis are just as likely to respond to rituximab therapy when compared to anti-TNF therapy,” said lead study author Duncan Porter. “However, a significant proportion of patients failed to respond to their first biologic drug, but responded when they were switched to the alternative. If we could identify markers in the blood that predicted which drug patients were most likely to respond to, that would allow us to choose the best treatment for that patient at the start, rather than rely on a trial-and-error approach.”

In the researchers’ own study, they first depleted ribosomal and globin RNA before sequencing RNA from the peripheral blood of 241 RA patients recruited for the ORBIT study. Seventy percent of the samples were used to develop response prediction models, while the other 30% was saved for validation.

Clinical response to the therapies was defined as a drop in disease activity score (DAS28-ESR) of 1.2 units between baseline and 3 months. To predict the general responsiveness and differential responses to TNFi and rituximab, researchers used multiple machine learning tools. Additionally, tenfold cross validation was used to train the models for responsiveness, and these were also tested on the validation samples.

With the help of support vector machine recursive feature elimination, researchers identified 3 gene expression signatures that predicted therapy responses. There were 8 genes that predicted general responsiveness to both TNFi and rituximab, while 23 genes predicted responsiveness to TNFi and 23 genes predicted responsiveness to rituximab.

“There are indeed gene expression markers that predict drug-specific response,” Porter said. “If confirmed, this will allow stratification of patients into groups more likely to respond to 1 drug rather than another. This would lead to higher response rates, and reduced likelihood of receiving a trial of an ineffective drug. Because ineffective treatment is associated with pain, stiffness, disability, and reduced quality of life, this will lead to better patient care.”

Researchers also tested the prediction models on the validation set. The results showed receiver operating characteristic (ROC) plot points with an area under the curve (AUC) of 91.6% for general responsiveness, 89.7% for TNFi response, and 85.7% for rituximab response.

The authors, who presented their findings at the 2016 ACR/ARHP annual meeting in Washington, noted that the next step in the process is to have confirmation of these models.

“The findings need to be confirmed using targeted RNA sequencing, or internal validation, and then tested in a new cohort of patients, or external validation,” Porter said. “Ultimately, a commercial testing kit would be developed to allow clinicians to test patients before they receive treatment to guide them to the most effective treatment.”