Selecting the right treatments for the right patients at the right time. Overall improved therapeutic outcomes for patients diagnosed with autoimmune disease.Challenges

• Immunomodulator therapies are ineffective in roughly half of patients diagnosed with autoimmune disease.
• Choosing the right treatment oftentimes involves a trial and error process, resulting in overall higher healthcare costs, morbidity, and mortality.

Benefits

• Selecting the right treatments for the right patients at the right time.
• Overall improved therapeutic outcomes for patients diagnosed with autoimmune disease.

ObjectivesTherapeutics for autoimmune diseases and rare diseases are expensive. Oftentimes, patients do not respond to treatment and yet are susceptible to side effects and adverse health outcomes. For example, Anti-TNFa therapies, while revolutionary in their ability to treat the disease, are only effective in roughly half of patients. 10-30% do not respond to initial treatment. Of the patients that do, 23-46% lose response over time. The goal is to identify a prognostic signature of treatment response that would empower clinicians to make better treatment decisions.SolutionsUGenome leverages a multicohort analysis framework which embraces the inherent variation found in real-world patient populations to identify biologically meaningful gene signatures that are predictive for therapy response. By analyzing gene expression data from multiple patient cohorts with clinician adjudicated response information, we can differentiate responders and non-responders. This has the potential to translate into clinic by developing prognostic tests that can guide clinician treatment decision making, addressing treatment inefficacies and costs.