Floris Chabrun, Daniel E. Schwartz, Susanna Gentile, Elias K. Mai, Tulika R. Gupta, Jacqueline Perry, David M. Cordas dos Santos, Thomas Hielscher, Annika Werly, Sophia K. Schmidt, Foteini Theodorakakou, Despina Fotiou, Christine Ivy Liacos, Nikolaos Kanellias, Noelia Collado Gisbert, Esperanza Martin-Sanchez, Rosalinda Termini, Johannes Waldschmidt, Selina J. Chavda, Louise Ainley, Matteo Claudio Da Vià, Claudio De Magistris, Loredana Pettine, Michael A. Timonian, Jean-Baptiste Alberge, Vidhi Patel, Patrick Costello, Catherine Tobia, Sally Phan, Jennifer Lamb, Maria-Theresa Silverio, Maya Davis, Elizabeth K. O’Donnell, Catherine R. Marinac, Omar Nadeem, Niccolo Bolli, Kwee Yong, K. Martin Kortüm, Hermann Einsele, María-Victoria Mateos, Shaji Kumar, Jesús F. San-Miguel, Bruno Paiva, Efstathios Kastritis, Meletios A. Dimopoulos, Marc S. Raab, Lorenzo Trippa, Irene M. Ghobrial
Accurate prediction of risk of progression from smoldering multiple myeloma (SMM) to active multiple myeloma (MM) is paramount to individualized early therapeutic strategies with minimum risk of overtreatment. Current risk stratification models do not account for evolving biomarker trajectories. We assembled a cohort of 2,344 patients with SMM from seven international centers with longitudinal clinical and biological data to train and validate the Precursor Asymptomatic Neoplasms by Group Effort Analysis (PANGEA)-SMM risk models. Four evolving biomarkers were significantly associated with shorter time to progression: M-protein increase ≥0.2 g dl−1, involved/uninvolved serum free light chain ratio increase ≥20, creatinine increase >25% and hemoglobin decrease ≥1.5 g dl−1. PANGEA-SMM outperforms established models, including the 20/2/20 and IMWG models, by more accurately predicting progression (C-statistic = 0.79), even without biomarker history (C-statistic = 0.78) or recent bone marrow biopsy (C-statistic = 0.78). We present PANGEA-SMM to the community as an easy-to-use, open-access tool for risk stratification in SMM. Validation tools are available to compare PANGEA-SMM to established models. Trained and validated on data from 2,344 patients with smoldering multiple myeloma, a new algorithm using longitudinal biomarker dynamics provides accurate prediction of risk of disease progression, outperforming established models.