Risk factors and their contributions to prognosis in multiple myeloma
Tae-Hoon Chung1, Jia Geng Chang2, Tze King Tan1
1Cancer Science Institute of Singapore, National University of Singapore.
Haematologica
|July 16, 2026
Summary
Identifying high-risk multiple myeloma (MM) is challenging. New genomic factors, beyond current recommendations, significantly improve survival prediction in MM patients, enabling earlier risk stratification.
Area of Science:
- Hematology
- Genomics
- Oncology
Background:
- Despite advances, early identification of high-risk multiple myeloma (MM) patients remains difficult.
- Existing risk stratification models need enhancement for improved patient outcomes.
Purpose of the Study:
- To evaluate the predictive utility of International Myeloma Society (IMS) / International Myeloma Working Group (IMWG) recommendations and novel genomic factors for high-risk MM.
- To assess the combined impact of multiple risk factors on patient survival.
Main Methods:
- Analysis of CoMMpass data incorporating IMS/IMWG lesions and four additional factors: APOBEC mutational activity, chromothripsis, EMC92 gene expression, and proliferation index (PR).
- Statistical modeling to assess the association of these factors with survival, adjusting for treatment heterogeneity.
- Evaluation of concordance (C-statistic) for risk prediction models.
Main Results:
- All assessed factors were significantly associated with survival.
- 68% of patients had high-risk (HR) lesions, with 62% having multiple lesions, demonstrating a 'multi-hit' effect on survival.
- The inclusion of additional HR factors improved risk prediction concordance from C=0.65 to C=0.72, with EMC92 being particularly crucial.
Conclusions:
- Expanding high-risk factors beyond current IMS/IMWG recommendations using diverse genomic technologies is essential for accurate MM patient risk stratification.
- The 'multi-hit' phenomenon underscores the need for comprehensive genomic profiling in MM.
- Early identification of high-risk MM patients can be significantly improved by integrating novel genomic markers.
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