The following is a summary of “Personalised progression prediction in patients with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma (PANGEA): a retrospective, multicohort study” published in the March 2023 issue of Haematology by Cowan, et al.
On the basis of monoclonal protein concentrations or bone marrow plasma cell percentage, patients with multiple myeloma precursors are dichotomized as having monoclonal gammopathy of unclear significance or smoldering multiple myeloma. Laboratory tests made at the time of diagnosis are used in current risk stratifications, but time-varying biomarkers are not considered. The purpose of this study was to create a risk modeling method for multiple myeloma progression based on easily accessible, time-varying biomarkers in patients with monoclonal gammopathy of uncertain significance and smoldering multiple myeloma. This retrospective multicohort research included patients with smoldering multiple myeloma or monoclonal gammopathy of unknown significance (MGUS) aged 18 and up. Using a training cohort, the researchers compared many models’ abilities to predict whether or not a patient with myeloma will develop the disease (with patients at Dana-Farber Cancer Institute, Boston, MA, USA; annotated from Nov 13, 2019, to April, 13, 2022).
The PANGEA models were developed to forecast the transition from precursor illness to multiple myeloma using biomarker data (monoclonal protein concentration, free light chain ratio, age, creatinine concentration, and bone marrow plasma cell percentage). Both the National and Kapodistrian University of Athens (Athens, Greece; January 26, 2020, to February 7, 2022; validation cohort 1) and University College London (London, United Kingdom; June 9, 2020, to April 10, 2022; validation cohort 1), as well as the Registry of Monoclonal Gammopathies, independently validated the models (Czech Republic, Czech Republic; Jan 5, 2004, to March 10, 2022; validation cohort 2). The Researchers compared the PANGEA models to the current criteria (the International Myeloma Working Group’s monoclonal gammopathy of uncertain significance and the 20/2/20 smoldering multiple myeloma risk criteria) with and without bone marrow data. The study included 6441 individuals, with 1,510 (23%) having active multiple myeloma and 4,931 (77%) having monoclonal gammopathy of uncertain significance.
Females comprised 3,430 (53%) of the total sample size of 6,441. The PANGEA model (BM) outperformed the 20/2/20 model in predicting the transition from smoldering to active multiple myeloma, with a C-statistic increase from 0.533 (0.480, 0.709) to 0.756 (0.629, 0.785) for the first clinic visit, 0.613 (0.504, 0.704), and 0.637 (0.386, 0.841), respectively, for the second and third visits in the validation cohort 1. To better predict which patients with smoldering multiple myeloma would develop the full-blown disease, the PANGEA model (no BM) outperformed the 20/2/20 model across all three-time points in the validation cohort 1: from visit 1 (0, 501-0, 672) to visit 3 (0, 691-736), and from visit 2 (0, 518-0, 647), to visit 3 (0, 693-736). With C-statistic increases from 640 (518-0718) to 729 (643-0941) for the PANGEA model (BM) and 670 (523-0729) to 879 (586-0938) for the PANGEA model (MM), the PANGEA models outperformed the IMWG rolling model at visit 1 in the validation cohort 2 for predicting monoclonal gammopathy of undetermined significance (no BM). When the PANGEA models are used in clinical practice, patients with precursor illnesses will have more precise assessments of their likelihood of progression to multiple myeloma, leading to better treatment decisions.
Source: sciencedirect.com/science/article/pii/S2352302622003866