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CU Data Scientist Receives Funds to Study Precision Medicine for Rheumatoid Arthritis

CU Data Scientist Receives Funds to Study Precision Medicine for Rheumatoid Arthritis

Fan Zhang, PhD, and her lab will identify new treatment targets for rheumatoid arthritis patients who aren’t responsive to common therapies.

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Written by Kara Mason on July 12, 2023

With no cure or widely successful therapeutic options for patients with rheumatoid arthritis (RA), Fan Zhang, PhD, is looking to computational machine learning to identify new target treatments.

Zhang, an assistant professor of medicine-rheumatology at the University of Colorado School of Medicine and a faculty member in the Department of Biomedical Informatics, received two grant awards this June that will foster a deeper look at personal medicine approaches to RA and the development of tools to make that possible.

One $250,000 grant Zhang and her lab received is from the Arthritis National Research Foundation (ANRF) to use computational omics to advance the field of translational medicine in autoimmune diseases. Zhang was named a 2023 Translational Research Scholar for her work in artificial intelligence methods for computational omics and systems immunology to study inflammatory disease pathogenesis, awarding her another $300,000 to further her work in that realm.

Zhang says the two grants, totaling $550,000 dollars, can help her and the researchers in her lab to advance personalized medicine approaches to identifying promising new targets to treat RA, ultimately improving the lives of patients who live with autoimmune disease.

“The ability to analyze large omics data could help lead to personalized treatments for patients who haven’t seen any success previously,” Zhang says.

Interdisciplinary research

Zhang is able to reach multi-disciplinaries and use artificial intelligence to discover pathogenic programs and molecular mechanisms to improve the treatment of inflammatory diseases.

“This funding helps build a bridge between these two fields,” she says. This new research will be built on breakthroughs Zhang and her team have already made in RA research with their key authored work published in Nature Immunology, Science Translational Medicine, Genome Medicine, and Frontiers Immunology in the past four years.  

“Using high-throughput single-cell sequencing technologies, we recently identified a unique immune cell population, a new macrophage phenotype, which is predominant in a subgroup of RA patients who usually fail the commonly used biologic treatment,” she explains. “This new macrophage phenotype also expresses high levels of key factors from the complement system, a central part of the human innate immunity that serves as a first line of defense against foreign invaders.”

Further investigation of this complement component-dependent macrophage phenotype could provide a novel treatment for patients of autoimmune disease like RA. 

Translational data-driven solutions

Machine learning is especially important to research of autoimmune diseases, Zhang says, because computer algorithms can analyze data far faster and more accurately than humans can and because RA is a complicated heterogeneous disease.

“RA is a prototypical autoimmune disease, so a patient can experience immune dysfunction throughout their entire body,” Zhang explains. “The disease can attack different tissues. It’s not just in joints, like the knee, but also in organs, like the lung and gut.”

That’s why researchers have difficulty pinpointing a target for treatment and are turning to personalized medicine. It also means creating tools that allow researchers to examine high-dimensional molecular and cellular data related to the autoimmune disease is a promising pathway.

The Zhang Lab works to build and push those tools forward so that researchers can take a more personalized approach to RA and help other inflammatory autoimmune disease patients who need it.

“Translational medicine is unique because we can now look at all ‘omics’ data with different biological information combined with clinical data from the human body and dig into the big data to integrate them together,” Zhang says. “Putting that all together can point to a more accurate target with the goal to translate into future clinical use.” 

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Fan Zhang, PhD