Genes Can Predict the Success of Arthritis Treatment

Scientists have discovered that the molecular profiling of diseased joint tissue may considerably influence whether certain drug treatments for rheumatoid arthritis (RA) patients will work.

A recent study demonstrates that genes may predict how well people respond to treatments for arthritis.

According to a new study from the Queen Mary University of London, molecular profiling of diseased joint tissue might greatly impact whether certain drug treatments will be effective in treating rheumatoid arthritis (RA) patients. The study was published in the journal Nature Medicine on May 19th, 2022.  The researchers also found certain genes related to resistance to most present drug therapies, often known as refractory disease, which might give the key to finding new, effective medicines to assist these patients.

While there has been substantial improvement in treating arthritis over the last decades, a large proportion of individuals (about 40%) do not respond to particular drug treatments, and 5-20% of persons with the condition are resistant to all existing kinds of medicine.

The researchers conducted a biopsy-based clinical study with 164 arthritis patients, testing their reactions to rituximab or tocilizumab — two medications routinely used to treat RA. The original trial’s findings, published in The Lancet in 2021, showed that in individuals with a low synovial B-cell molecular signature, just 12% reacted to a treatment that targets B cells (rituximab), whereas 50% responded to an alternate medication (tocilizumab). Both medications were equally effective when patients had high amounts of this genetic signature.

As part of the first-of-its-kind study, funded by the Efficacy and Mechanism Evaluation (EME) Programme, an MRC and NIHR partnership, the Queen Mary team also looked at the cases where patients did not respond to treatment via any of the drugs and found that there were 1,277 genes that were unique to them specifically.

Building on this, the researchers applied a data analysis technique called machine learning models to develop computer algorithms that could predict drug responses in individual patients. The machine learning algorithms, which included gene profiling from biopsies, performed considerably better at predicting which treatment would work best compared to a model which used only tissue pathology or clinical factors.

The study strongly supports the case for performing gene profiling of biopsies from arthritic joints before prescribing expensive so-called biologic targeted therapies. This could save the NHS and society considerable time and money and help avoid potential unwanted side effects, joint damage, and worse outcomes that are common among patients. As well as influencing treatment prescription, such testing could also shed light on which people may not respond to any of the current drugs on the market, emphasizing the need for developing alternative medications.

Professor Costantino Pitzalis, Versus Arthritis Professor of Rheumatology at the Queen Mary University of London, said: “Incorporating molecular information prior to prescribing arthritis treatments to patients could forever change the way we treat the condition. 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.

“These results are incredibly exciting in demonstrating the potential at our fingertips, however, the field is still in its infancy and additional confirmatory studies will be required to fully realize the promise of precision medicine in RA.

“The results are also important in finding solutions for those people who unfortunately don’t have a treatment that helps them presently. Knowing which specific molecular profiles impact this, and which pathways continue to drive disease activity in these patients, can help in developing new drugs to bring better results and much-needed relief from pain and suffering.”

The incorporation of these signatures in future diagnostic tests will be a necessary step to translate these findings into routine clinical care.

Reference: “Rituximab versus tocilizumab in rheumatoid arthritis: synovial biopsy-based biomarker analysis of the phase 4 R4RA randomized trial” by Felice Rivellese, Anna E. A. Surace, Katriona Goldmann, Elisabetta Sciacca, Cankut Çubuk, Giovanni Giorli, Christopher R. John, Alessandra Nerviani, Liliane Fossati-Jimack, Georgina Thorborn, Manzoor Ahmed, Edoardo Prediletto, Sarah E. Church, Briana M. Hudson, Sarah E. Warren, Paul M. McKeigue, Frances Humby, Michele Bombardieri, Michael R. Barnes, Myles J. Lewis, Costantino Pitzalis, and the R4RA collaborative group, 19 May 2022, Nature Medicine.
DOI: 10.1038/s41591-022-01789-0

ArthritisDrugsGeneticsMedicineQueen Mary University of London