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Using patient data, a predictive algorithm can identify the risk of progression to multiple myeloma in those with asymptomatic precursor conditions. This algorithm can help clinicians decide when to initiate treatment and which patients require closer monitoring.

  • People with multiple myeloma precursor conditions are likely to develop symptomatic multiple myeloma.
  • Predicting the disease severity of multiple myeloma is a challenge, but can help patients access treatment sooner.
  • A novel PANGEA algorithm has been developed that can use patient data to predict disease progression and prognosis in patients with a precursor condition.

Multiple myeloma is a cancer of plasma cells often preceded by asymptomatic precursor conditions. Two such conditions: monoclonal gammopathy of undetermined significance and smoldering multiple myeloma, are determined by presence of abnormalities in the blood and bone marrow plasma. By the time patients experience symptoms, their disease has typically already progressed to multiple myeloma.

Predicting Disease Outcomes in Multiple Myeloma

Being able to predict disease outcomes creates more opportunities for intervention. New methods are being developed to determine a patient’s risk level and prognosis using biomarkers, but current techniques do not account for how a biomarker changes throughout disease progression. A new PANGEA model recently described in The Lancet Haematology, improves predictive power by incorporating time-varying biomarkers.

The PANGEA Model: Outperforming Standard Models 

Laboratory measurements from a cohort of patients with a precursor condition were used to develop the PANGEA model. Using trends in clonal proliferation and clinically significant markers of disease progression, the PANGEA model outperformed the standard predictive models. The final model, designated PANGEA model [BM], included age, free light chain ratio, M spike concentration, creatinine concentration, bone marrow plasma cell percentage, and the hemoglobin trajectory variable.

Advancing Risk Assessment in Multiple Myeloma

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PANGEA provides a more accurate estimate of risk progression than current prognostic models. Compared to the baseline and rolling 20/2/20 models, the PANGEA model demonstrated enhanced predictive ability for the progression from smoldering multiple myeloma to multiple myeloma. Even in patients who did not have bone marrow biopsies, the PANGEA model performed better than existing models. With improved risk assessment, patients can more accurately be assessed for disease progression and prognosis, prompting appropriate treatment as early as possible.

Source: 

Cowan, A., Ferrari, F., Freeman, S. S., Redd, R., El-Khoury, H., Perry, J., Patel, V., Kaur, P., Barr, H., Lee, D. J., Lightbody, E., Downey, K., Argyelan, D., Theodorakakou, F., Fotiou, D., Liacos, C. I., Kanellias, N., Chavda, S. J., Ainley, L., . . . Ghobrial, I. M. (2023). Personalised progression prediction in patients with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma (PANGEA): a retrospective, multicohort study. Lancet Haematol, 10(3), e203-e212. https://doi.org/10.1016/s2352-3026(22)00386-6