Multiple myeloma (MM) is a heterogeneous plasma cell disorder characterized by genetic abnormalities, including chromosomal translocations, deletions, duplications, and mutations. Sever conditions precede the diagnosis of active MM. Monoclonal gammopathy of undetermined significance (MGUS) is mostly a premalignant disorder and smoldering multiple myeloma (SMM) is generally thought to bridge the gap between MGUS and MM. As MGUS and SMM are generally not treated, it is unknown if not treating is more dangerous than treating. There is a need for risk assessment models that allow physicians to determine whether these pre-active MM disorders should be treated.
Current risk assessment models are based on the size and type of abnormal M-protein, the serum free light chain level, the free light chain ratio, the extent of bone marrow plasmacytosis, presence and numbers of focal lesions by modern imaging, flow cytometry-based immunotype, and gene expression profiles of plasma cells. However, there remains a need for a readily available, inexpensive and effective risk model to determine the best treatment for pre-active MM disorders. Within this invention are methods of developing a predictive model to determine the risk of patients with pre-active MM for progressing to active MM requiring treatment. The method uses fluorescence in-situ hybridization (FISH) to identify low- and high-risk pre-active MM patients. The predictive model also allows follow-up analysis to determine changes in risk and response to therapy. It may also be used to identify relapses in MM patients that achieved a complete response following therapy and thus have a need for immediate intervention. The method requires a minimal amount of biological sample without the need for purification of plasma cells from the sample.