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Temporal genomic dynamics shape clinical trajectory in multiple myeloma

Nature Genetics volume 57, pages 2203–2214 (2025)Cite this article

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Abstract

Multiple myeloma evolution is characterized by the accumulation of genomic drivers over time. To unravel this timeline and its impact on clinical outcomes, we analyzed 421 whole-genome sequences from 382 patients. Using clock-like mutational signatures, we estimated a time lag of two to four decades between the initiation of events and diagnosis. We demonstrate that odd-numbered chromosome trisomies in patients with hyperdiploidy can be acquired simultaneously with other chromosomal gains (for example, 1q gain). We show that hyperdiploidy is acquired after immunoglobulin heavy chain translocation when both events co-occur. Finally, patients with early 1q gain had adverse outcomes similar to those with 1q amplification (>1 extra copy), but fared worse than those with late 1q gain. This finding underscores that the 1q gain prognostic impact depends more on the timing of acquisition than on the number of copies gained. Overall, this study contributes to a better understanding of the life history of myeloma and may have prognostic implications.

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Fig. 1: Multiple myeloma mutational signatures landscape.
Fig. 2: Molecular time and temporal relationship between large chromosomal gains in multiple myeloma.
Fig. 3: Multiple myeloma molecular times.
Fig. 4: Contribution of mutational signatures in duplicated and nonduplicated mutations.
Fig. 5: Timing the initiation of multiple myeloma.
Fig. 6: Timing of canonical IGH and MYC translocations.
Fig. 7: Timing and patterns of 1q gain and amplification.

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Data availability

GMMG-HD6 has been uploaded on EGA—EGAD50000000681, EGAD50000000682 and EGAD50000000683. MSKCC WGS data have been uploaded to EGA—EGAD00001011132. RRMM WGS data have been uploaded to EGA—EGAS00001006538, EGAS00001004363, EGAS00001004805 and EGAS00001005973. Source data are provided with this paper.

Code availability

The code used for the timing analysis is provided in Supplementary Data 2 and on GitHub at https://github.com/bachisiozic/Timing-the-first-multi-gain-events-in-Multiple-Myeloma/tree/main.

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Acknowledgements

This work was supported by the Myeloma Solutions Fund, Paula and Rodger Riney Multiple Myeloma Research Program Fund, the Tow Foundation, Sylvester Comprehensive Cancer Center National Cancer Institute (NCI) Core Grant (P30 CA 240139), Memorial Sloan Kettering Cancer Center NCI Core Grant (P30 CA 008748) and NYU NCI Core Grant (P30CA016087). F.M. was supported by the Leukemia and Lymphoma Society (LLS), the International Myeloma Society (IMS), the Department of Defence and the National Institutes of Health (NIH)–NCI. G.J.M. received grant support through a Translational Research Program award from the LLS (6020-20). K.H.M. has received funding from the Multiple Myeloma Research Foundation (MMRF), the American Society of Hematology and the IMS. A.M.P. is funded by the Medical Data Scientist Program of Heidelberg University, Faculty of Medicine. N.W. is funded by the Advanced Medical Scientist Program of Heidelberg University, Faculty of Medicine. The Heidelberg Team thanks the Sample Processing Lab, the High-Throughput Sequencing Unit of the Genomics & Proteomics Core Facility and the Omics IT and Data Management Core Facility of the German Cancer Research Center (DKFZ), the DKFZ–Heidelberg Center for Personalized Oncology (DKFZ–HIPO) office, the Biobank Multiple Myeloma UKHD and the Myeloma Registry for excellent services. Support and funding of the project provided by the Dietmar Hopp Foundation and the NCT Heidelberg Molecular Precision Oncology Program (project K08K) is gratefully acknowledged. Data storage service provided by SDS@hd is supported by the Ministry of Science, Research and the Arts Baden-Württemberg (MWK) and the German Research Foundation (DFG) through grants INST 35/1314-1 FUGG and INST 35/1503-1 FUGG. L.B.B. is supported by NIH–NCI (R37CA272883).

Author information

Author notes

  1. These authors contributed equally: Francesco Maura, Marcella Kaddoura, Alexandra M. Poos, Marc S. Raab, Niels Weinhold.

Authors and Affiliations

  1. Myeloma Institute, Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA

    Francesco Maura, Marcella Kaddoura, Bachisio Ziccheddu, Anthony Cirrincione, Monika Chojnacka, Benjamin Diamond, Marios Papadimitriou & Ola Landgren

  2. Myeloma Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York City, NY, USA

    Francesco Maura, Kylee Maclachlan, Alexander M. Lesokhin, Neha Korde & Saad Usmani

  3. Heidelberg Myeloma Center, Department of Medicine V, University Hospital Heidelberg, Medical Faculty, Heidelberg University, Heidelberg, Germany

    Alexandra M. Poos, Marc-Andrea Bärtsch, Lukas John, Philipp Reichert, Stefanie Huhn, Elias K. Mai, Marc S. Raab & Niels Weinhold

  4. Clinical Cooperation Unit Molecular Hematology/Oncology, Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg University and German Cancer Research Center (DKFZ), Heidelberg, Germany

    Alexandra M. Poos, Marc-Andrea Bärtsch, Lukas John, Marc S. Raab & Niels Weinhold

  5. Division of Hematopathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

    Linda B. Baughn

  6. Myeloma Research Program, NYU Langone, Perlmutter Cancer Center, New York City, NY, USA

    Patrick Blaney, Dylan Gagler, Faith Davies & Gareth J. Morgan

  7. Cytogenetics Laboratory, Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, NY, USA

    Yanming Zhang

  8. Hematopathology Service, Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, NY, USA

    Ahmet Dogan

  9. Internal Medicine V, Hematology, Oncology and Rheumatology, GMMG Study Group, Heidelberg University Hospital and the National Center for Tumor Diseases (NCT), Heidelberg, Germany

    Hartmut Goldschmidt

  10. Department of Hematology, Oncology and Clinical Immunology, University Hospital Duesseldorf, Duesseldorf, Germany

    Roland Fenk

  11. Department of Oncology, Hematology, and Blood and Marrow Transplant, University Medical Center Hamburg—Eppendorf, Hamburg, Germany

    Katja C. Weisel

  12. Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA

    S. Vincent Rajkumar & Shaji Kumar

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  1. Francesco Maura
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  2. Marcella Kaddoura
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  3. Alexandra M. Poos
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  21. Hartmut Goldschmidt
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  22. Roland Fenk
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  27. S. Vincent Rajkumar
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  30. Ola Landgren
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  32. Niels Weinhold
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Contributions

F.M., M.S.R. and N.W. designed and supervised the study. F.M., M.S.R., N.W., M.K., A.M.P., K.M., M.C., B.D., P.B., L.J., P.R., S.H., M.A.B., D.G., Y.Z., F.D., G.M. and A.D. collected data. F.M., M.S.R., N.W., M.K. and A.M.P. generated the data and wrote the manuscript. F.M., M.S.R., N.W., M.K., A.M.P., B.Z., A.C. and M.P. analyzed the data. N.K., S.U., O.L., E.K.M., H.G., K.C.W., A.L. and R.F. supervised the clinical trials and collected clinical data. V.R., S.K. and L.B. collected and analyzed the FISH data from Mayo Clinic.

Corresponding authors

Correspondence to Francesco Maura or Niels Weinhold.

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Competing interests

O.L. has received research funding from NIH, NCI, U.S. Food and Drug Administration, MMRF, International Myeloma Foundation, LLS, Myeloma Solutions Fund, Paula and Rodger Riney Multiple Myeloma Research Program Fund, the Tow Foundation, Perelman Family Foundation, Rising Tide Foundation, Amgen, Celgene, Janssen, Takeda, Glenmark, Seattle Genetics, Karyopharm; Honoraria/ad boards: Adaptive, Amgen, Binding Site, Bristol Myers Squibb (BMS), Celgene, Cellectis, Glenmark, Janssen, Juno, Pfizer; and serves on Independent Data Monitoring Committees for clinical trials lead by Takeda, Merck, Janssen, Theradex. G.J.M. has received funding from NIH, NCI, MMRF, LLS, Perelman Family Foundation, Amgen, Celgene, Janssen and Takeda; has received honoraria or advisory board fees from Adaptive, Amgen, BMS, Celgene and Janssen; and serves on Independent Data Monitoring Committees for clinical trials led by Takeda, Karyopharm and Sanofi. E.K.M. reports consulting or advisory role, honoraria, research funding, travel accommodation and expenses from BMS (Celgene), GlaxoSmithKline (GSK), Janssen-Cilag, Oncopeptides, Pfizer, Sanofi, Stemline and Takeda. K.C.W. reports research grant from AbbVie, Amgen, BMS/Celgene, Janssen, GSK and Sanofi; and has received honoraria and consulting fees from AbbVie, Amgen, Adaptive Biotech, AstraZeneca, BMS/Celgene, BeiGene, Janssen, GSK, Karyopharm, Novartis, Oncopeptides, Pfizer, Roche Pharma, Sanofi, Stemline and Takeda. R.F. reports consulting or advisory role, honoraria, travel accommodation and expenses from Amgen, BMS (Celgene), GSK, Janssen-Cilag, Sanofi, Stemline and Takeda. K.H.S. has served on an advisory board for AbbVie, Amgen, BMS, GSK and Janssen; received honoraria for Adaptive Biotechnologies Corporation, Amgen, BMS, GSK, Janssen and Sanofi Genzyme; and has received research funding from AbbVie and Karyopharm Therapeutics. H.G. has received grants and/or provision of investigational medicinal product from BMS/Celgene, Dietmar-Hopp-Foundation, Janssen and Sanofi; research support from Amgen, BMS, Celgene, GlycoMimetics, GSK, Heidelberg Pharma, Hoffmann-La Roche, Janssen, Millenium Pfizer, Sanofi and Novartis; advisory board fees from BMS, GSK, Janssen and Sanofi; honoraria from Amgen, BMS, GSK, Janssen, Oncopeptides, Sanofi and Pfizer; and support for attending meetings and/or travel form Amgen, BMS, GSK, Janssen, Oncopeptides, Sanofi and Pfizer. N.K. has received research funding from Janssen and AbbVie, and has served on an advisory board for Janssen. K.H.M. has received funding from the Multiple Myeloma Research Foundation, the American Society of Hematology and the IMS. L.B.B. served as a consultant for Genentech. F.M. has received consulting fees from Medidata and Sanofi. B.D. reports consulting via an independent data review committee for Janssen. All other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Study cohort genomic landscape.

The 12 newly diagnosed multiple myeloma (NDMM) genomic groups (ref. 33) and their distribution across the MSKCC and German cohorts included in this study. All defining features of the 12 groups are depicted. Heatmap colors: gray = wild type; red = monoallelic involvement; brown = biallelic involvement. Immunoglobulin heavy chain (IGH) canonical translocations are reported in brown. Presence of APOBEC and hyper-APOBEC is reported in red and brown, respectively. HY: hyperdiploid.

Extended Data Fig. 2 Impact of APOBEC on multiple myeloma clinical outcomes.

a, Comparison of mutational burden between the MSKCC and German cohorts included in this study. The P value was calculated using the Wilcoxon test. SBS: single base substitution. b,c, Impact of Hyper-APOBEC presence on progression-free survival (PFS) and overall survival (OS) in newly diagnosed multiple myeloma patients.

Extended Data Fig. 3 Clock-like signatures in B and myeloma cells.

a, Correlation between SBS1 and SBS5 mutational burden (clock-like signatures) and patients’ age at sample collection. Samples that did not fit into the linear model reported in Fig. 1b are plotted with transparency (residual > 1,900). Three samples with >8,000 SBS1 and SBS5 mutations were excluded for graphical purpose (MM119, MM165, and RRMM48). b, Comparison of SBS1 and SBS5 clock mutation rates between multiple myeloma and B-cell lymphoma. c, Comparison of SBS1 and SBS5 clock mutation rate between multiple myeloma (red line) and normal memory B cells (black line). Given the single cell colony expansion in normal memory B-cell data (ref. 51), only clonal variants were considered for multiple myeloma. SBS: single base substitution; NDMM: newly diagnosed multiple myeloma; RRMM: relapsed refractory multiple myeloma; Mel+ and Mel−: melphalan-exposed or not-exposed RRMM patients, respectively.

Extended Data Fig. 4 SBS mutational signature contribution in relapsed refractory multiple myeloma (RRMM).

This figure illustrates the contribution of SBS mutational signatures in RRMM, distinguishing between clonal and subclonal variants. P values were estimated using the Wilcoxon test.

Extended Data Fig. 5 APOBEC mutagenesis across time.

a, Comparison of APOBEC contribution in clonal variants between hyper-APOBEC and non-hyper-APOBEC. b–g, SBS mutational signature contribution among clonal and subclonal variants in samples with hyper-APOBEC.

Extended Data Fig. 6 Temporal relationship of distinct chromosomal aberrations.

a) Examples of molecular time estimates of HY patients with 1q gain. In the top case, the 1q was acquired at the same time as the HY multi-gain event. The middle case shows a 1q gain acquired at the same time as the HY multi-gain event, but later compared to the first case. The last case (bottom) has a 1q gain acquired independently after the HY multi-gain event. b–d) The molecular time estimates of chromosomal gains shared, selected or lost over time in patients with WGS data available at two different time points. c) Boxplot of estimated molecular time for gains of non-HY chromosomes (1, 2, 4, 6, 8, 10, 12, 13, 14, 16, 17, 18, 20, and 22) based on the absence or presence of canonical IGH translocations. c) Boxplot of estimated molecular time for gains of odd-numbered chromosomes typically seen in hyperdiploid myeloma (3, 5, 7, 9, 11, 15, 19, and 21) based on the presence (left box) or absence (right box) of canonical IGH translocations. d) Boxplot of estimated molecular time for LOH events in HY patients, divided into odd-numbered HY chromosomes (left box), and non-HY chromosomes (right box). Comparisons were performed using the Wilcoxon test.

Extended Data Fig. 7 Temporal patterns of chromosomal gains across the 12 genomic subgroups of multiple myeloma (ref. 33).

a, Estimated molecular time of all chromosomal duplications across the 12 genomic subgroups. b, Estimated molecular timeof odd-numbered hyperdiploid chromosomal duplications across the 12 genomic subgroups, considering only patients with a hyperdiploid profile. c, Estimated molecular time of 1q gains across the 12 genomic subgroups. Adjusted P values for comparisons in a–c are reported in Supplementary Table 8.

Extended Data Fig. 8 Timing of focal deletions and gains at the MYC locus.

a,b, A schematic illustration summarizing the workflow for distinguishing between pregain (a) and postgain (b) deletions, as well as preduplications (c) and postduplications (d). e,f, Two possible temporal scenarios in which an MYC translocation occurs either after (e) or before (f) large chromosomal gains.

Extended Data Fig. 9 Impact of timing of acquisition.

a,b, Kaplan–Meier plots for progression-free survival (PFS; a) and overall survival (OS; b) for the following three groups: early 1q gain (molecular time ≤ 0.85), late 1q gain (molecular time > 0.85) and 1q amp. Compared to Fig. 6c,d, we show here the confidence intervals of the Kaplan–Meier curves.

Extended Data Fig. 10 Timing copy number gains with chemotherapy mutational signatures.

Cartoon illustrating how the melphalan mutagenic mutational signature SBS99 can be used to estimate the timing of large chromosomal gains. If 1q gain was pre-existing, SBS99 would only be detected among the nonduplicated mutations on 1q. In contrast, if 1q gain was acquired after HDM–ASCT, SBS99 mutations on one allele would be duplicated and SBS99 would be detectable among the duplicated variants. The figure is created with BioRender.com.

Supplementary information

Reporting Summary

Peer Review File

Supplementary Tables 1–14

Supplementary Tables 1–14.

Supplementary Data 1

Timing multiple myeloma evolution.

Supplementary Data 2

Timing the first multi-gain events in multiple myeloma.

Supplementary Data 3

Phylogenetic tree of multiple myeloma patients with WGS available at two different time points.

Supplementary Data 4

Timing multiple myeloma evolution.

Source data

Source Data Fig. 1

Data used to generate Fig. 1a–c.

Source Data Figs. 2 and 3

Data used to generate Figs 2b–j and 3.

Source Data Fig. 4

Data used to generate Fig. 4.

Source Data Fig. 5

Data used to generate Fig. 5.

Source Data Fig. 6

Data used to generate Fig. 6e.

Source Data Fig. 7

Data used to generate Fig. 7.

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Maura, F., Kaddoura, M., Poos, A.M. et al. Temporal genomic dynamics shape clinical trajectory in multiple myeloma. Nat Genet 57, 2203–2214 (2025). https://doi.org/10.1038/s41588-025-02292-1

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