Development and validation of a simplified score to predict early relapse in newly diagnosed multiple myeloma in a pooled dataset of 2,190 patients.

Gian Maria Zaccaria, Luca Bertamini, Maria Teresa Petrucci, Massimo Offidani, Paolo Corradini, Andrea Capra, Alessandra Romano, Anna Marina Liberati, Donato Mannina, Paolo de Fabritiis, Nicola Cascavilla, Marina Ruggeri, Roberto Mina, Francesca Patriarca, Giulia Benevolo, Angelo Belotti, Gianluca Gaidano, Arnon Nagler, Roman Hajek, Andrew SpencerPieter Sonneveld, Pellegrino Musto, Mario Boccadoro, Francesca Gay

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: Despite the improvement of therapeutic regimens, several patients with multiple myeloma (MM) still experience early relapse (ER). This subset of patients currently represents an unmet medical need. Experimental Design: We pooled data from seven European multicenter phase II/III clinical trials enrolling 2,190 patients with newly diagnosed MM from 2003 to 2017. Baseline patient evaluation included 14 clinically relevant features. Patients with complete data (n = 1,218) were split into training (n = 844) and validation sets (n = 374). In the training set, a univariate analysis and a multivariate logistic regression model on ER within 18 months (ER18) were made. The most accurate model was selected on the validation set. We also developed a dynamic version of the score by including response to treatment. Results: The Simplified Early Relapse in Multiple Myeloma (S-ERMM) score was modeled on six features weighted by a score: 5 points for high lactate dehydrogenase or t(4;14); 3 for del17p, abnormal albumin, or bone marrow plasma cells >60%; and 2 for l free light chain. The S-ERMM identified three patient groups with different risks of ER18: Intermediate (Int) versus Low (OR = 2.39, P < 0.001) and High versus Low (OR = 5.59, P < 0.001). S-ERMM High/Int patients had significantly shorter overall survival (High vs. Low: HR = 3.24, P < 0.001; Int vs. Low: HR = 1.86, P < 0.001) and progression-free survival-2 (High vs. Low: HR = 2.89, P < 0.001; Int vs. Low: HR = 1.76, P < 0.001) than S-ERMM Low. The Dynamic S-ERMM (DS-ERMM) modulated the prognostic power of the S-ERMM. Conclusions: On the basis of simple, widely available baseline features, the S-ERMM and DS-ERMM properly identified patients with different risks of ER and survival outcomes.
Original languageEnglish
Pages (from-to)3695-3703
Number of pages9
JournalClin. Cancer Res.
Volume27
Issue number13
DOIs
Publication statusPublished - Jul 1 2021

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