Genomic Classification and Individualized Prognosis in Multiple Myeloma

TY - JOUR

T1 - Genomic Classification and Individualized Prognosis in Multiple Myeloma

AU - Maura, Francesco

AU - Rajanna, Arjun Raj

AU - Ziccheddu, Bachisio

AU - Poos, Alexandra M

AU - Derkach, Andriy

AU - Maclachlan, Kylee

AU - Durante, Michael

AU - Diamond, Benjamin

AU - Papadimitriou, Marios

AU - Davies, Faith

AU - Boyle, Eileen M

AU - Walker, Brian

AU - Hultcrantz, Malin

AU - Silva, Ariosto

AU - Hampton, Oliver

AU - Teer, Jamie K

AU - Siegel, Erin M

AU - Bolli, Niccolò

AU - Jackson, Graham H

AU - Kaiser, Martin

AU - Pawlyn, Charlotte

AU - Cook, Gordon

AU - Kazandjian, Dickran

AU - Stein, Caleb

AU - Chesi, Marta

AU - Bergsagel, Leif

AU - Mai, Elias K

AU - Goldschmidt, Hartmut

AU - Weisel, Katja C

AU - Fenk, Roland

AU - Raab, Marc S

AU - Van Rhee, Fritz

AU - Usmani, Saad

AU - Shain, Kenneth H

AU - Weinhold, Niels

AU - Morgan, Gareth

AU - Landgren, Ola

PY - 2024/4/10

Y1 - 2024/4/10

N2 - PURPOSE: Outcomes for patients with newly diagnosed multiple myeloma (NDMM) are heterogenous, with overall survival (OS) ranging from months to over 10 years.METHODS: To decipher and predict the molecular and clinical heterogeneity of NDMM, we assembled a series of 1,933 patients with available clinical, genomic, and therapeutic data.RESULTS: Leveraging a comprehensive catalog of genomic drivers, we identified 12 groups, expanding on previous gene expression-based molecular classifications. To build a model predicting individualized risk in NDMM (IRMMa), we integrated clinical, genomic, and treatment variables. To correct for time-dependent variables, including high-dose melphalan followed by autologous stem-cell transplantation (HDM-ASCT), and maintenance therapy, a multi-state model was designed. The IRMMa model accuracy was significantly higher than all comparator prognostic models, with a c-index for OS of 0.726, compared with International Staging System (ISS; 0.61), revised-ISS (0.572), and R2-ISS (0.625). Integral to model accuracy was 20 genomic features, including 1q21 gain/amp, del 1p, TP53 loss, NSD2 translocations, APOBEC mutational signatures, and copy-number signatures (reflecting the complex structural variant chromothripsis). IRMMa accuracy and superiority compared with other prognostic models were validated on 256 patients enrolled in the GMMG-HD6 (ClinicalTrials.gov identifier: NCT02495922) clinical trial. Individualized patient risks were significantly affected across the 12 genomic groups by different treatment strategies (ie, treatment variance), which was used to identify patients for whom HDM-ASCT is particularly effective versus patients for whom the impact is limited.CONCLUSION: Integrating clinical, demographic, genomic, and therapeutic data, to our knowledge, we have developed the first individualized risk-prediction model enabling personally tailored therapeutic decisions for patients with NDMM.

AB - PURPOSE: Outcomes for patients with newly diagnosed multiple myeloma (NDMM) are heterogenous, with overall survival (OS) ranging from months to over 10 years.METHODS: To decipher and predict the molecular and clinical heterogeneity of NDMM, we assembled a series of 1,933 patients with available clinical, genomic, and therapeutic data.RESULTS: Leveraging a comprehensive catalog of genomic drivers, we identified 12 groups, expanding on previous gene expression-based molecular classifications. To build a model predicting individualized risk in NDMM (IRMMa), we integrated clinical, genomic, and treatment variables. To correct for time-dependent variables, including high-dose melphalan followed by autologous stem-cell transplantation (HDM-ASCT), and maintenance therapy, a multi-state model was designed. The IRMMa model accuracy was significantly higher than all comparator prognostic models, with a c-index for OS of 0.726, compared with International Staging System (ISS; 0.61), revised-ISS (0.572), and R2-ISS (0.625). Integral to model accuracy was 20 genomic features, including 1q21 gain/amp, del 1p, TP53 loss, NSD2 translocations, APOBEC mutational signatures, and copy-number signatures (reflecting the complex structural variant chromothripsis). IRMMa accuracy and superiority compared with other prognostic models were validated on 256 patients enrolled in the GMMG-HD6 (ClinicalTrials.gov identifier: NCT02495922) clinical trial. Individualized patient risks were significantly affected across the 12 genomic groups by different treatment strategies (ie, treatment variance), which was used to identify patients for whom HDM-ASCT is particularly effective versus patients for whom the impact is limited.CONCLUSION: Integrating clinical, demographic, genomic, and therapeutic data, to our knowledge, we have developed the first individualized risk-prediction model enabling personally tailored therapeutic decisions for patients with NDMM.

U2 - 10.1200/JCO.23.01277

DO - 10.1200/JCO.23.01277

M3 - SCORING: Journal article

C2 - 38194610

VL - 42

SP - 1229

EP - 1240

JO - J CLIN ONCOL

JF - J CLIN ONCOL

SN - 0732-183X

IS - 11

ER -