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Genomic Classification and Individualized Prognosis in Multiple Myeloma

Genomic Classification and Individualized Prognosis in Multiple Myeloma

January 9, 2024 | Francesco Maura, MD1; Arjun Raj Rajanna, MSc2; Bachisio Ziccheddu, MSc1; Alexandra M. Poos, PhD2,3; Andriy Derkach, PhD4; Kylee MacLachlan, MD, PhD5; Michael Durante, MD, PhD1; Benjamin Diamond, MD1; Marios Papadimitriou, MD1; Faith Davies, MD6; Eileen M. Boyle, MD1; Brian Walker, PhD7; Malin Hultcrantz, MD, PhD8; Ariosto Silva, PhD9; Oliver Hampton, PhD9; Jamie K. Teer, PhD10; Erin M. Siegel, PhD11; Niccolò Bolli, MD, PhD12,13; Graham H. Jackson, MD, PhD14; Martin Kaiser, MD15; Charlotte Pawlyn, MD, PhD16; Gordon Cook, MD, PhD17; Dickran Kazandjian, MD18; Caleb Stein, PhD19; Marta Chesi, PhD1; Leif Bergsagel, MD, PhD1; Elias K. Mai, MD1; Hartmut Goldschmidt, MD20; Katja C. Weisel, MD18; Roland Fenk, MD, PhD19; Marc S. Raab, MD23; Fritz Van Rhee, MD, PhD20; Saad Usmani, MD5; Kenneth H. Shain, MD, PhD8; Niels Weinhold, PhD2,3; Gareth Morgan, MD, PhD6; and Ola Landgren, MD, PhD1
This study aims to develop an individualized risk-prediction model for newly diagnosed multiple myeloma (NDMM) to enable personalized therapeutic decisions. The authors leveraged a large dataset of 1,933 patients with NDMM, integrating clinical, demographic, genomic, and treatment data. They identified 12 distinct biological groups based on genomic features and developed the Individualized Risk-Prediction Model for NDMM (IRMMa). IRMMa demonstrated superior accuracy compared to existing prognostic models (International Staging System [ISS], revised [R]-ISS, and R2-ISS) in predicting overall survival (OS) and event-free survival (EFS). IRMMa integrates 20 highly relevant genomic features, improving the identification of primary refractory and early progressive patients. It also allows for the estimation of treatment variance, which can guide the selection of the most effective therapy. The model was validated in the GMMG-HD6 trial, showing high concordance between predicted and observed outcomes. IRMMa represents a significant advancement in the field, providing a more comprehensive and accurate tool for individualized risk prediction in NDMM.This study aims to develop an individualized risk-prediction model for newly diagnosed multiple myeloma (NDMM) to enable personalized therapeutic decisions. The authors leveraged a large dataset of 1,933 patients with NDMM, integrating clinical, demographic, genomic, and treatment data. They identified 12 distinct biological groups based on genomic features and developed the Individualized Risk-Prediction Model for NDMM (IRMMa). IRMMa demonstrated superior accuracy compared to existing prognostic models (International Staging System [ISS], revised [R]-ISS, and R2-ISS) in predicting overall survival (OS) and event-free survival (EFS). IRMMa integrates 20 highly relevant genomic features, improving the identification of primary refractory and early progressive patients. It also allows for the estimation of treatment variance, which can guide the selection of the most effective therapy. The model was validated in the GMMG-HD6 trial, showing high concordance between predicted and observed outcomes. IRMMa represents a significant advancement in the field, providing a more comprehensive and accurate tool for individualized risk prediction in NDMM.
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