The HemOnc Pulse Under the Hood: Exploring the Genomic Engines of Smoldering Myeloma
Oct 17, 2025
Dr. Omar Nadeem, a Senior Physician at Dana-Farber and Assistant Professor at Harvard, shares cutting-edge developments in smoldering myeloma. He discusses how genomic profiling can refine risk stratification and highlight high-risk patients, allowing for personalized treatment. The role of innovative models like PANGEA and AI in dynamic risk assessment is explored, alongside exciting insights into bispecific antibodies and their early successes in trials. Dr. Nadeem emphasizes the shift towards early detection and intervention strategies to improve patient outcomes.
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Practical First-Line Risk Screening
- Use the 20-20-20 criteria (≥20% marrow plasma cells, ≥2 g/dL M protein, light-chain ratio >20) as a quick initial risk screen for smoldering myeloma.
- Monitor patients longitudinally and apply dynamic models like PANGEA when serial data suggest evolving risk.
Value Of Dynamic Risk Models
- Dynamic changes matter more than single timepoint measures when assessing progression risk.
- Omar Nadeem expects AI and machine learning to integrate longitudinal data for improved risk prediction.
Early Immunotherapy With Appropriate Prophylaxis
- Consider testing immunotherapies like BCMA bispecifics earlier in high-risk smoldering myeloma trials because they may work better and safer before heavy pretreatment.
- Manage prophylaxis similarly to relapse setting, including IVIG, antiviral, and PJP prophylaxis to reduce infection risk.