Genomic Classification and Individualized Prognosis in Multiple Myeloma

Area of Science:

• Hematology
• Genomics
• Oncology

Background:

• Multiple myeloma (MM) outcomes are highly variable.
• Newly diagnosed MM (NDMM) patients exhibit diverse survival rates, necessitating better risk stratification.

Purpose of the Study:

• To develop an individualized risk-prediction model for NDMM.
• To integrate clinical, genomic, and therapeutic data for enhanced prognostic accuracy.
• To guide personalized treatment strategies in NDMM.

Main Methods:

• Assembled a cohort of 1,933 NDMM patients with comprehensive data.
• Utilized genomic drivers to define 12 molecular groups.
• Developed a multi-state model incorporating clinical, genomic, and treatment variables, including high-dose melphalan with autologous stem-cell transplantation (HDM-ASCT).

Main Results:

• The individualized risk in multiple myeloma (IRMMa) model achieved a c-index of 0.726 for overall survival (OS), outperforming existing models (ISS, revised-ISS, R2-ISS).
• Key predictors included 20 genomic features such as 1q21 gain/amp, del 1p, TP53 loss, NSD2 translocations, and APOBEC/copy-number signatures.
• IRMMa accuracy was validated in the GMMG-HD6 trial, demonstrating superior predictive power and identifying treatment variances across genomic groups.

Conclusions:

• Developed the first individualized risk-prediction model for NDMM by integrating diverse patient data.
• The IRMMa model enables personalized therapeutic decisions for NDMM patients.
• This approach enhances treatment tailoring based on individual risk profiles and genomic characteristics.