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Updated: Jan 9, 2026

Establishment of a Human Multiple Myeloma Xenograft Model in the Chicken to Study Tumor Growth, Invasion and Angiogenesis
Published on: May 1, 2015
Evolution of multiple myeloma from a genomic perspective.
Francesco Maura1, Mehmet K Samur2, Nikhil C Munshi3,4
1Myeloma Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, NY.
Understanding the progression from precursor conditions like monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM) to multiple myeloma (MM) involves complex genomic, environmental, and immune factors. Early detection and prediction require integrating diverse data for high-risk patients.
Area of Science:
- Oncology
- Genetics
- Immunology
Background:
- Multiple myeloma (MM) universally follows precursor states, such as monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM), often decades prior to diagnosis.
- Genetic predisposition, including germline variants and MM-specific loci, significantly influences initial transformation and risk.
- Disparities in MM and precursor condition incidence across racial groups underscore the importance of genetic predisposition and necessitate broader research cohorts.
Purpose of the Study:
- To explore the intricate interplay of genomic evolution, environmental exposures, genetic predispositions, and immune surveillance in the progression from MGUS and SMM to MM.
- To highlight the complexity of myelomagenesis and emphasize the need for advanced prediction strategies.
Main Methods:
- Review of existing literature on genomic events, environmental factors, and immune responses in MM development.
- Integration of genomic and transcriptomic data with immune profiling and clinical features for risk stratification.
Main Results:
- Early genomic events like translocations and hyperdiploidy are critical for precursor initiation.
- Progression to symptomatic MM requires additional factors, including the positive selection of subclonal populations, influenced by aging and environmental exposures (e.g., Agent Orange, agrochemicals).
Conclusions:
- Identifying patients at high risk of MM progression necessitates integrating genomic, transcriptomic, and immune profiling data with clinical features.
- State-of-the-art approaches are crucial for improving the prediction of MM development from precursor conditions.
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