Genomic Classification and Individualized Prognosis in Multiple Myeloma

Published on January 09, 2024

Authors: Francesco Maura, MD, Arjun Raj Rajanna, MSc, Bachisio Ziccheddu, MSc, Alexandra M. Poos, PhD, Andriy Derkach, PhD, Kylee Maclachlan, MD, PhD, Michael Durante, MD, PhD, and Ola Landgren, MD, PhD

Published in the Journal of Clinical Oncology, this study aims to decipher and predict the molecular and clinical heterogeneity of newly diagnosed multiple myeloma (NDMM) to improve individualized prognosis.

The study assembled a series of 1,933 patients with available clinical, genomic, and therapeutic data. By leveraging a comprehensive catalog of genomic drivers, the researchers identified 12 groups, expanding on previous gene expression–based molecular classifications.

To build a model predicting individualized risk in NDMM (IRMMa), the researchers integrated clinical, genomic, and treatment variables. A multi-state model was designed to correct for time-dependent variables, including high-dose melphalan followed by autologous stem-cell transplantation (HDM-ASCT) and maintenance therapy.

The IRMMa model accuracy was found to be significantly higher than all comparator prognostic models, with a c-index for overall survival (OS) of 0.726, compared with International Staging System (ISS; 0.61), revised-ISS (0.572), and R2-ISS (0.625).

The study identified 20 genomic features integral to model accuracy, including 1q21 gain/amp, del 1p, TP53 loss, NSD2 translocations, APOBEC mutational signatures, and copy-number signatures reflecting complex structural variant chromothripsis.

The accuracy and superiority of IRMMa compared with other prognostic models were validated on 256 patients enrolled in the GMMG-HD6 clinical trial (ClinicalTrials.gov identifier: NCT02495922).

According to the study, individualized patient risks were significantly affected across the 12 genomic groups by different treatment strategies, which was used to identify patients for whom HDM-ASCT is particularly effective versus patients for whom the impact is limited.

In conclusion, the integration of clinical, demographic, genomic, and therapeutic variables has led to the development of the IRMMa model, which shows promising accuracy in predicting individualized risk in NDMM. This advancement could potentially impact treatment decisions and improve outcomes for patients with multiple myeloma.