Personalized Predictive Model for Treating Multiple Myeloma

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Multiple myeloma presents a significant challenge for treatment. While there have been advancements in therapy, determining the most effective treatment for each patient is complex due to the variability of the disease from person to person. In an article published in the Journal of Clinical Oncology, researchers from the Moffitt Cancer Center, Sylvester Comprehensive Cancer Center, and global groups have released findings from a new model that offers personalized predictions for individual patient responses to different therapies.

Multiple myeloma is a rare blood cancer that affects plasma cells, and finding appropriate therapeutic management is crucial as there is currently no cure for the disease. To assist in determining the best therapeutic approach, clinicians currently use prognostic tools such as fluorescence in situ hybridization (FISH) and gene expression profiling models. However, these tests have limitations according to Ken Shain, Ph.D., M.D., the lead study author and co-leader of the Pentecost Family Myeloma Research Center at Moffitt.

To address these limitations, a team of researchers developed a new genomic classification system for multiple myeloma that categorizes patients into 12 distinct groups based on their genomic profiles. The resulting individualized risk model in multiple myeloma (IRMMa) incorporates advanced sequencing techniques and statistical methodologies to generate personalized overall survival and event-free survival predictions for multiple myeloma patients.

The study was supported by various organizations including the National Cancer Institute, Myeloma Solutions Fund, the Paula and Rodger Riney Multiple Myeloma Research Program Fund, and others.

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