Research Appraisalobservational

Genomics Define Malignant Transformation in Myeloma Precursor Conditions

J Clin OncolMaura, Francesco, Bergsagel, P Leif, Ziccheddu, Bachisio et al.1 Jan 2026DOI

Clinical Snapshot

75CEBM
Evidence: Moderateobservational

PICO Framework

P — Population374 patients with MGUS or SMM (277 training cohort, 97 validation cohort)
I — InterventionGenomic analysis to identify malignant transformation markers
C — ComparatorTraditional clinical criteria for progression (end-organ damage)
O — OutcomesProgression to multiple myeloma, genomic classification accuracy, risk stratification improvement

Bottom Line

This prognostic study introduces a genomic classification system that may revolutionise risk stratification in myeloma precursor conditions. By identifying 'genomic MM' versus 'genomic MGUS', clinicians could potentially distinguish patients who are biologically malignant but asymptomatic from those with truly premalignant conditions. The finding that most SMM patients have genomic features indistinguishable from MM challenges current classification systems. While promising, the clinical utility depends on validation in larger cohorts with longer follow-up, cost-effectiveness analysis, and standardisation of genomic testing protocols. Australian haematologists should await full publication details before implementing this approach, particularly regarding specific genomic markers, testing methodology, and integration with existing risk models. The potential to reduce unnecessary surveillance in low-risk patients while intensifying monitoring in high-risk cases represents a significant advance in precision medicine for myeloma precursors.

Evidence: Moderate

Key Findings

  • P Value: Not provided in abstract

  • Effect Size: 60% of MGUS and 10% of SMM classified as genomic MGUS (no malignant transformation)

  • Primary Outcome: Genomic classification distinguishing malignant from premalignant precursor conditions

  • Nnt Or Sensitivity: Most SMM patients showed genomic features indistinguishable from MM; genomic MGUS showed no progression during follow-up

  • Confidence Interval: Not provided in abstract

Clinical Application

Requires genomic sequencing capabilities, likely feasible in major Australian cancer centres Could inform PBS funding decisions for genomic testing in haematological malignancies; relevant to specialist haematology practice in tertiary centres Patients with MGUS or SMM requiring risk stratification for progression monitoring

Abstract

Multiple myeloma (MM) is consistently preceded by monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). While these precursor conditions are asymptomatic, they are not entirely benign and carry a lifelong risk of progression to MM. Unlike other cancers defined by pathology, malignant transformation from MGUS or SMM to MM has so far relied on demonstration of clinical end-organ damage as morphology and cytogenetics cannot reliably distinguish them. In this study, using genomic data from 374 patients with MGUS or SMM (277 training, 97 validation), to our knowledge, we demonstrate for the first time the ability to identify malignant transformation in MGUS and SMM. We introduce the concept of genomic MM and genomic MGUS to differentiate the subsets of MGUS and SMM that are biologically malignant with genomic features indistinguishable from MM from the subset that is premalignant and unlikely to progress to malignancy. Importantly, we find that most SMM has biological features of malignant transformation indistinguishable from MM. As expected, this subset that we consider having genomic MM is associated with a high risk of progression to MM although some patients remained progression-free beyond 5 years. Conversely, 60% of MGUS and 10% of SMM have no evidence of malignant transformation (genomic MGUS), with no progression during follow-up. Integration of genomic features with the 2/20/20 International Myeloma Working Group model significantly improved the prediction of progression among genomic MM. These findings support the use of genomic criteria to refine the classification and the risk stratification in myeloma precursor conditions.

References

  1. 1.Maura, F., Bergsagel, P. L., Ziccheddu, B., Kumar, S., Maclachlan, K., Derkach, A., Garces, J. J., Firestone, R., Braggio, E., Asmann, Y., Durante, M., Diamond, B. T., Papadimitriou, M., Hultcrantz, M., Marella, A., Castellano, G., Maeda, A., Lionetti, M., Matera, A., Pioggia, S., Da Vià, M. C., de Magistris, C., Leongamornlert, D., DeAvila, D., Sudalagunta, P. R., Canevarolo, R. R., Siegel, E. M., Agius, P., Teer, J., McPherson, A., Yamashita, Y., Silva, A. S., Blaney, P., Baz, R., Patel, K. K., Campbell, P., Morgan, G., Fonseca, R., Landgren, O., Orlowski, R. Z., Shain, K. H., Bolli, N., Usmani, S., & Rajkumar, S. V. (2026). Genomics define malignant transformation in myeloma precursor conditions. Journal of Clinical Oncology. https://doi.org/10.1182/blood.2024026019
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