The UAMS Myeloma Institute is the only facility in the world that routinely offers gene array analysis for newly referred patients and utilizes this information for patient management and planning of therapy. The Myeloma Institute performs multiple gene arrays on patients enrolled on investigator-initiated clinical trials.
Researchers at the Myeloma Institute use state-of-the-art gene array analysis to characterize molecular features of myeloma. They can apply this knowledge to more accurately predict which patients will benefit the most from specific therapies.
The Myeloma Institute performs multiple gene arrays on every patient enrolled on a clinical protocol. Based on a study of more than 500 newly diagnosed patients treated at the Myeloma Institute for multiple myeloma, our researchers found that the expression of just 17 genes (out of the 25,000 genes in the human body) revealed which form of myeloma a patient had. The expression level of those 17 genes serves as a powerful predictor of response to therapy. This enables doctors to more accurately predict which patients will not respond to standard therapy and thereby spare patients from undergoing treatments that will not be effective. The discovery is also important to the development of new treatments that specifically target the 17 genes.
The newest GEP project led by Christoph Hueck, MD, involves the analysis of prospectively collected clinical data as well as tissue specimens from the approximately 10,000 patients treated at the Myeloma Institute since 1989. While GEP has been applied to a portion of specimens using modern high-throughput techniques, numerous samples collected prior to the introduction of GEP technology have not been processed. The project involves processing the untouched bone marrow aspirate and biopsy samples using GEP. Analysis of clinical data and tissue specimens will yield a more in-depth understanding of the biology of myeloma cells and their microenvironment.
The GEP data is being biostatistically integrated with clinical data in order to gain insight about factors that predict response to treatment as well as treatment targets based on pathogenesis.