Genetic analysis could reveal which RA patients will respond to treatment, a new study suggests. Photo by Taokinesis/Pixabay
May 19 (UPI) -- Genetic profiling of the diseased joint tissue can identify whether specific drug treatments will work to treat rheumatoid arthritis, a study published Thursday found.
It could also help identify rheumatoid arthritis, or RA, sufferers with genetic mutations that make their symptoms resistant to currently available prescription drug treatments, the researchers said in an article published Thursday by the journal Nature Medicine.
This form of RA is often called refractory disease, and understanding which genes lead to it could provide the key to developing new, more effective treatments, they said.
"Incorporating molecular information prior to prescribing arthritis treatments to patients could forever change the way we treat the condition," Dr. Costantino Pitzalis, the study co-author, said in a press release.
"Patients would benefit from a personalized approach that has a far greater chance of success, rather than the trial-and-error drug prescription that is currently the norm," said Pitzalis, a professor of rheumatology at Queen Mary University of London.
About 40% of people with RA do not respond to specific drug therapies, while up to 20% of people with the disease are resistant to all current forms of medication, research suggests.
For this study, the researchers conducted a biopsy-based clinical trial with 164 arthritis patients, in which their responses to either rituximab or tocilizumab -- two drugs commonly used to treat RA -- were tested from samples collected from the affected joints.
Of participants with a low synovial B-cell molecular signature, 12% responded to rituximab, while 50% responded to tocilizumab, the data showed.
However, when patients had high levels of this genetic signature, the two drugs were similarly effective, the researchers said.
Synovial B-cells are immune cells found in the fluid surrounding joints, such as the knee, they said.
In RA patients who did not respond to treatment with any currently available drugs, analyses revealed they had nearly 1,300 unique genes that may help prevent response, according to the researchers.
A machine-learning model built using this data was able to accurately predict which treatment would work best in select patients, the researchers said.
Incorporating these signatures in future diagnostic tests will be a necessary step to translate these findings into routine clinical care, they said.
"These results are incredibly exciting in demonstrating the potential at our fingertips, [but] the field is still in its infancy," Pitzalis said.
"The results are also important in finding solutions for those people who unfortunately don't have a treatment that helps them presently," he said.