The PANGEA model: catching the drift from precursor conditions to myeloma in individual patients.

Collective definition efforts in the field of plasma cell disorders have shaped the classification of precursor conditions into two groups—monoclonal gammopathy of undetermined significance and smouldering multiple myeloma—based on a handful of basic parameters measured at diagnosis.1Rajkumar SV Dimopoulos MA Palumbo A et al.International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma.Lancet Oncol. 2014; 15: e538-e548Summary Full Text Full Text PDF PubMed Scopus (2809) Google Scholar This snapshot can dichotomise patients into low-risk and high-risk categories with a 1% and 5–10% progression risk per annum, respectively. Although the definition is highly effective, at 5 years there are still 20% of patients with smouldering multiple myeloma have not progressed and 5% with monoclonal gammopathy of undetermined significance have progressed, heralding the need for more effective risk stratification approaches. Numerous attempts of risk stratifications have been made in both monoclonal gammopathy of undetermined significance and smouldering multiple myeloma, most of them using a combination of markers derived from blood and bone marrow. Among them, the Mayo Clinic used abnormal serum free light chain (FLC) ratios (ratio <0·26 or >1·65), elevated non-IgG concentration, or elevated M-protein concentration (monoclonal protein; >15 g/L) to stratify patients with monoclonal gammopathy of undetermined significance and suggested that the risk of progression to multiple myeloma at 20 years was 5% with no risk factors, 21% with one risk factor, 37% with two risk factors, and 58% with three risk factors present.2Rajkumar SV Kyle RA Therneau TM et al.Serum free light chain ratio is an independent risk factor for progression in monoclonal gammopathy of undetermined significance.Blood. 2005; 106: 812-817Crossref PubMed Scopus (568) Google Scholar When it comes to smouldering multiple myeloma, the same group stratified patients into risk categories based on an FLC ratio greater than 20, an elevated M-protein concentration greater than 2 g/dL, and bone marrow plasma cell percentage (BMPC%) of more than 20%. These criteria allowed the stratification of patients into categories of low risk (no risk factor), intermediate risk (one risk factor), or high risk (two or more risk factors). This 20/2/20 stratification system was updated by the International Myeloma Working Group (IMWG) to include high-risk cytogenetics—ie, presence of t(4;14), t(14;16), gain(1q), or del (13/13q).3Mateos M-V Kumar S Dimopoulos MA et al.International Myeloma Working Group risk stratification model for smoldering multiple myeloma (SMM).Blood Cancer J. 2020; 10: 102Crossref PubMed Scopus (93) Google Scholar Other risk models have suggested that the proportion of bone marrow aberrant plasma cells within the bone marrow plasma cell compartment, DNA aneuploidy, immunoparesis,4Pérez-Persona E Vidriales M-B Mateo G et al.New criteria to identify risk of progression in monoclonal gammopathy of uncertain significance and smoldering multiple myeloma based on multiparameter flow cytometry analysis of bone marrow plasma cells.Blood. 2007; 110: 2586-2592Crossref PubMed Scopus (411) Google Scholar imaging patterns,5Hillengass J Fechtner K Weber M-A et al.Prognostic significance of focal lesions in whole-body magnetic resonance imaging in patients with asymptomatic multiple myeloma.J Clin Oncol. 2010; 28: 1606-1610Crossref PubMed Scopus (286) Google Scholar cell-free DNA,6Deshpande S Tytarenko RG Wang Y et al.Monitoring treatment response and disease progression in myeloma with circulating cell-free DNA.Eur J Haematol. 2021; 106: 230-240Crossref PubMed Scopus (14) Google Scholar expression profiles,7Boyle EM Rosenthal A Ghamlouch H et al.Plasma cells expression from smouldering myeloma to myeloma reveals the importance of the PRC2 complex, cell cycle progression, and the divergent evolutionary pathways within the different molecular subgroups.Leukemia. 2021; 36: 591-595Crossref PubMed Scopus (3) Google Scholar and genomic profiles8Boyle EM Deshpande S Tytarenko R et al.The molecular make up of smoldering myeloma highlights the evolutionary pathways leading to multiple myeloma.Nat Commun. 2021; 12: 293Crossref PubMed Scopus (46) Google Scholar could also be used clinically. Despite being highly effective, none of these models captured the time component, with the notable exception of Fernández de Larrea and colleagues’ criteria,9Fernández de Larrea C Isola I Pereira A et al.Evolving M-protein pattern in patients with smoldering multiple myeloma: impact on early progression.Leukemia. 2018; 32: 1427-1434Crossref PubMed Scopus (43) Google Scholar which incorporated dynamic monitoring of the paraprotein into a smouldering multiple myeloma risk stratification model. In a large collaborative work in The Lancet Haematology, Annie Cowan and colleagues10Cowan A Ferrari F Freeman SS et al.Personalised progression prediction in patients with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma (PANGEA): a retrospective, multicohort study.Lancet Haematol. 2023; 10: e203-e212Summary Full Text Full Text PDF PubMed Scopus (4) Google Scholar attempted to make sense of all these models and build the PANGEA model. The backbone of the PANGEA model can be used to improve risk assessment for patients with precursor conditions such as monoclonal gammopathy of undetermined significance and smouldering multiple myeloma to develop overt multiple myeloma. By combining age, serum creatinine concentration, and haemoglobin levels over time with known predictors readily available in the clinic, such as paraprotein levels and FLC ratios, with or without BMPC%, Cowan and colleagues10Cowan A Ferrari F Freeman SS et al.Personalised progression prediction in patients with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma (PANGEA): a retrospective, multicohort study.Lancet Haematol. 2023; 10: e203-e212Summary Full Text Full Text PDF PubMed Scopus (4) Google Scholar show that the PANGEA model can significantly improve accuracy over standard models. Furthermore, removing the need for a bone marrow assessment and additional expensive tests makes this tool easily manageable, even in resource-limited settings, adding to its strengths. Among parameters, changes in haemoglobin are included, which allows physicians to spot progressors that would have been otherwise overseen by static models, thus adding precision to the individual risk assessment and subsequent patient management. Finally, although it is something considered in routine clinical practice, none of the other models considered age. This consideration is of particular relevance, especially for patients younger than 40 years or older than 85 years, and might help adjust both monitoring and treatment strategies in these groups. In the future, other parameters such as cytogenetics, circulating tumour cells, and cell-free DNA, as well as imaging, clinical (eg, BMI, race, ethnicity, or even environmental risk factors), and genomic data, could be built into the PANGEA model. Lastly, pooling all the information in an online user-fliendly tool, such as the one developed by Cowan and colleagues,10Cowan A Ferrari F Freeman SS et al.Personalised progression prediction in patients with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma (PANGEA): a retrospective, multicohort study.Lancet Haematol. 2023; 10: e203-e212Summary Full Text Full Text PDF PubMed Scopus (4) Google Scholar might help reduce the caveat, shared by many models, induced by missing data. In conclusion, the work by Cowan and colleagues10Cowan A Ferrari F Freeman SS et al.Personalised progression prediction in patients with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma (PANGEA): a retrospective, multicohort study.Lancet Haematol. 2023; 10: e203-e212Summary Full Text Full Text PDF PubMed Scopus (4) Google Scholar is an exciting addition to the risk-assessing tools in monoclonal gammopathy of undetermined significance and smouldering multiple myeloma. By using data derived from blood draws alongside dynamic variables, the PANGEA model improves the accuracy of current models in a user-friendly way, is readily applicable in routine clinical practice, and constitutes the backbone for the establishment of future models. We declare no competing interests. Personalised progression prediction in patients with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma (PANGEA): a retrospective, multicohort studyUse of the PANGEA models in clinical practice will allow patients with precursor disease to receive more accurate measures of their risk of progression to multiple myeloma, thus prompting for more appropriate treatment strategies. Full-Text PDF Open Access