Genome-Wide Somatic Alterations in Multiple Myeloma Reveal a Superior Outcome Group

Mehmet Kemal Samur, Anil Aktas Samur, Mariateresa Fulciniti, Raphael Szalat, Tessa Han, Masood Shammas, Paul Richardson, Florence Magrangeas, Stephane Minvielle, Jill Corre, Philippe Moreau, Anjan Thakurta, Kenneth C Anderson, Giovanni Parmigiani, Hervé Avet-Loiseau, Nikhil C Munshi, Mehmet Kemal Samur, Anil Aktas Samur, Mariateresa Fulciniti, Raphael Szalat, Tessa Han, Masood Shammas, Paul Richardson, Florence Magrangeas, Stephane Minvielle, Jill Corre, Philippe Moreau, Anjan Thakurta, Kenneth C Anderson, Giovanni Parmigiani, Hervé Avet-Loiseau, Nikhil C Munshi

Abstract

Purpose: Multiple myeloma (MM) is accompanied by heterogeneous somatic alterations. The overall goal of this study was to describe the genomic landscape of myeloma using deep whole-genome sequencing (WGS) and develop a model that identifies patients with long survival.

Methods: We analyzed deep WGS data from 183 newly diagnosed patients with MM treated with lenalidomide, bortezomib, and dexamethasone (RVD) alone or RVD + autologous stem cell transplant (ASCT) in the IFM/DFCI 2009 study (ClinicalTrials.gov identifier: NCT01191060). We integrated genomic markers with clinical data.

Results: We report significant variability in mutational load and processes within MM subgroups. The timeline of observed activation of mutational processes provides the basis for 2 distinct models of acquisition of mutational changes detected at the time of diagnosis of myeloma. Virtually all MM subgroups have activated DNA repair-associated signature as a prominent late mutational process, whereas APOBEC signature targeting C>G is activated in the intermediate phase of disease progression in high-risk MM. Importantly, we identify a genomically defined MM subgroup (17% of newly diagnosed patients) with low DNA damage (low genomic scar score with chromosome 9 gain) and a superior outcome (100% overall survival at 69 months), which was validated in a large independent cohort. This subgroup allowed us to distinguish patients with low- and high-risk hyperdiploid MM and identify patients with prolongation of progression-free survival. Genomic characteristics of this subgroup included lower mutational load with significant contribution from age-related mutations as well as frequent NRAS mutation. Surprisingly, their overall survival was independent of International Staging System and minimal residual disease status.

Conclusion: This is a comprehensive study identifying genomic markers of a good-risk group with prolonged survival. Identification of this patient subgroup will affect future therapeutic algorithms and research planning.

Figures

FIG 1.
FIG 1.
Overall and genomic region mutational loads for patients with newly diagnosed multiple myeloma (MM). (A) Barplot shows the total number of somatic alterations (y-axis) per patients. Single-nucleotide variants (SNVs) are shown on top panel, insertions (INS) and deletions (DEL) are stacked at the bottom panel for each patient, and samples are ordered from highest number of SNVs to lowest. (B) Number of SNVs and InDels identified per megabase of the MM genome. (C) Number of mutations per megabase (y-axis) among MM subgroups (x-axis). (D) Number of mutations per megabase (y-axis) among genomic regions for all MM (left) and t(14;16) subgroups (right). HMM, hyperdiploid multiple myeloma; UTR, untranslated region.
FIG 2.
FIG 2.
Genome-wide mutational signatures in newly diagnosed multiple myeloma (MM). (A) Eight mutational signatures identified by nonnegative matrix factorization using all samples. Contributions (y-axis) of each signature (color coded) are shown for each patient (x-axis). Samples ordered from highest number of single-nucleotide variants to lowest. (B) Contribution (y-axis) of mutational signatures (each panel) in each MM subgroup (x-axis). P values for analysis of variance (ANOVA) test that compares the differences among group means are given on the top. (C) Contribution (y-axis) of each mutational signature (color-coded lines) from clonal mutations to subclonal mutations (x-axis). (D) Relative changes in signature contributions (y-axis) from clonal to subclonal mutations (x-axis) for hyperdiploid multiple myeloma (HMM; left) and del13, t(4;14), t(6;14), and t(8;14) (right) subgroups. (E) Relative changes in signature contributions (y-axis) from clonal to subclonal mutations (x-axis) for del17p, gain1q, del1p, and t(11;14) subgroups. (F) Two MM mutational signature activation models from early to late stage of the disease. Top panel represents the sequence of the processes for subgroups listed in D, and bottom panel represents the model for subgroups that are given in E. SBS, single-base substitution.
FIG 2.
FIG 2.
Genome-wide mutational signatures in newly diagnosed multiple myeloma (MM). (A) Eight mutational signatures identified by nonnegative matrix factorization using all samples. Contributions (y-axis) of each signature (color coded) are shown for each patient (x-axis). Samples ordered from highest number of single-nucleotide variants to lowest. (B) Contribution (y-axis) of mutational signatures (each panel) in each MM subgroup (x-axis). P values for analysis of variance (ANOVA) test that compares the differences among group means are given on the top. (C) Contribution (y-axis) of each mutational signature (color-coded lines) from clonal mutations to subclonal mutations (x-axis). (D) Relative changes in signature contributions (y-axis) from clonal to subclonal mutations (x-axis) for hyperdiploid multiple myeloma (HMM; left) and del13, t(4;14), t(6;14), and t(8;14) (right) subgroups. (E) Relative changes in signature contributions (y-axis) from clonal to subclonal mutations (x-axis) for del17p, gain1q, del1p, and t(11;14) subgroups. (F) Two MM mutational signature activation models from early to late stage of the disease. Top panel represents the sequence of the processes for subgroups listed in D, and bottom panel represents the model for subgroups that are given in E. SBS, single-base substitution.
FIG 3.
FIG 3.
Low genomic scar score (GSS) predicts the superior outcome in multiple myeloma (MM). (A) Loss of Heterozygosity–Homologous Recombination Deficiency (LOH-HRD), Large-Scale Transitions (LST), and Number of Telomeric Allelic Imbalances (TelomericAI) scores are given in the bottom (rows) for each patient (columns). Red indicates higher scores. Known translocations and copy number alterations (CNAs) are given in the middle panel as present (indicated with different colors for different alterations) or absent. Total GSS is given on the top panel. (B) Forest plot shows the odds ratio (with 95 CI) of translocations and CNAs between low-GSS and high-GSS groups. Number of samples affected by each alteration in each group is shown in the second (low-GSS group) and third (high-GSS group) columns. P values from Fisher’s exact test are given at the last column. (C) A decision tree from recursive partitioning analysis. For groups (low GSS with [w/] gain 9, low GSS without [w/o] gain 9, high GSS with t(4;14) or gain1q or no gain9 and the rest) frequencies in the IFM/DFCI cohort are given in each final node, with total number of samples and number of deaths at the end of the study. Relative risk for each terminal node is calculated compared with overall cohort. (D) Kaplan-Meier plot shows the overall survival probability for 4 groups defined by recursive partitioning in IFM/DFCI cohort. (E) Kaplan-Meier plot shows the overall survival probability for 4 groups in independent MM Research Foundation Relating Clinical Outcomes in MM to Personal Assessment of Genetic Profile validation cohort. (F) Kaplan-Meier plot shows the progression-free survival (PFS) for the patients in the low-GSS + gain9 group. Patients are divided by treatment arm. (G) Kaplan-Meier plots show the PFS for the patients in the high-GSS + t(4;14) or gain1q or no gain9 group. Patients are divided by treatment arm. ASCT, autologous stem cell transplant; HMM, hyperdiploid multiple myeloma; OR, odds ratio.
FIG 3.
FIG 3.
Low genomic scar score (GSS) predicts the superior outcome in multiple myeloma (MM). (A) Loss of Heterozygosity–Homologous Recombination Deficiency (LOH-HRD), Large-Scale Transitions (LST), and Number of Telomeric Allelic Imbalances (TelomericAI) scores are given in the bottom (rows) for each patient (columns). Red indicates higher scores. Known translocations and copy number alterations (CNAs) are given in the middle panel as present (indicated with different colors for different alterations) or absent. Total GSS is given on the top panel. (B) Forest plot shows the odds ratio (with 95 CI) of translocations and CNAs between low-GSS and high-GSS groups. Number of samples affected by each alteration in each group is shown in the second (low-GSS group) and third (high-GSS group) columns. P values from Fisher’s exact test are given at the last column. (C) A decision tree from recursive partitioning analysis. For groups (low GSS with [w/] gain 9, low GSS without [w/o] gain 9, high GSS with t(4;14) or gain1q or no gain9 and the rest) frequencies in the IFM/DFCI cohort are given in each final node, with total number of samples and number of deaths at the end of the study. Relative risk for each terminal node is calculated compared with overall cohort. (D) Kaplan-Meier plot shows the overall survival probability for 4 groups defined by recursive partitioning in IFM/DFCI cohort. (E) Kaplan-Meier plot shows the overall survival probability for 4 groups in independent MM Research Foundation Relating Clinical Outcomes in MM to Personal Assessment of Genetic Profile validation cohort. (F) Kaplan-Meier plot shows the progression-free survival (PFS) for the patients in the low-GSS + gain9 group. Patients are divided by treatment arm. (G) Kaplan-Meier plots show the PFS for the patients in the high-GSS + t(4;14) or gain1q or no gain9 group. Patients are divided by treatment arm. ASCT, autologous stem cell transplant; HMM, hyperdiploid multiple myeloma; OR, odds ratio.
FIG 4.
FIG 4.
Genomic differences among 4 groups defined by recursive partitioning. (A) Number of total mutations per patient in each group; (B) total genomic loss (deletion) in each subgroup; (C) contribution of single-base substitution 1 (SBS1; age related), and (D) SBS8 (DNA repair) mutational signatures; and (E) driver mutation enrichment analysis. ANOVA, analysis of variance; GSS, genomic scar score; w/, with; w/o, without.
FIG A1.
FIG A1.
Relative contribution of 6 possible mutation types. (A) Frequency (y-axis) of each mutation type (colors are different mutation types) for each patient at diagnosis. (B) Relative contributions of 96 possible trinucleotides at diagnosis. For each trinucleotide context, mean value is shown with color-coded bars, and standard deviations are shown with error lines.
FIG A2.
FIG A2.
Genome-wide mutational signatures of 183 patients with newly diagnosed multiple myeloma (MM). (A) Eight mutational signatures identified by nonnegative matrix factorization using all samples. Contribution of each trinucleotide context (y-axis) is shown, and error bars are given. Each signature is shown in a separate panel. (B) Cosine distance between 8 mutational signatures extracted from the MM cohort and single-base substitution (SBS) signatures provided on Cosmic Mutational Signature V3.
FIG A3.
FIG A3.
Absolute contribution (No. of mutations, y-axis) of 8 mutational signatures to multiple myeloma (MM) subgroups (x-axis). Each signature is shown in panels and ordered by overall contribution to MM. ANOVA, analysis of variance; HMM, hyperdiploid multiple myeloma; SBS, single-base substitution.
FIG A4.
FIG A4.
Contribution of each mutational signature in multiple myeloma subgroups from clonal to subclonal mutations. HMM, hyperdiploid multiple myeloma; SBS, single-base substitution.
FIG A5.
FIG A5.
Contribution of each mutational signature in multiple myeloma subgroups from clonal to subclonal mutations. Box plots are clustered for signatures at clonality levels and use the same color codes. Each box plot at clonality level shows the distribution of signature contribution among the patients belonging to each subcategory (panels from top to bottom). HMM, hyperdiploid multiple myeloma; SBS, single-base substitution.
FIG A6.
FIG A6.
Relative changes of each mutational signature contribution in multiple myeloma subgroups from clonal to subclonal mutations. Box plots are clustered for signatures at clonality levels and use the same color codes. Each box plot at clonality level shows the relative contribution (relative to clonal) of mutational signatures among the patients belonging to each subcategory (panels from top to bottom). HMM, hyperdiploid multiple myeloma; SBS, single-base substitution.
FIG A7.
FIG A7.
Genomic scar score (GSS) is associated with progression-free survival (PFS) and overall survival (OS). (A) PFS probability of low-GSS (red) and high-GSS (blue) groups. (B) OS probability of low-GSS (L-GSS; red) and high-GSS (H-GSS; blue) groups. ASCT, autologous stem cell transplant.
FIG A8.
FIG A8.
Genomic scar score (GSS) is associated with overall survival (OS). (A) OS probability of all 6 segments defined by recursive partitioning. (B) Kaplan-Meier plot shows the OS probability for 4 groups defined by recursive partitioning in IFM/DFCI cohort. Patients in each group divided by treatment arm they were assigned to. (C) Kaplan-Meier plot shows the OS probability for patients with hyperdiploid multiple myeloma (HMM) in 4 groups defined by recursive partitioning in the IFM/DFCI cohort. (D) Kaplan-Meier plot shows the overall survival probability for patients with HMM in 4 groups defined by recursive partitioning in the MM Research Foundation CoMMpass data set. w/, with; w/o, without.
FIG A9.
FIG A9.
Kaplan-Meier plots show progression-free survival (PFS) probability for patients with multiple myeloma in 4 groups defined by recursive partitioning in the IFM/DFCI cohort. Patients in each group are divided by the treatment arm to which they were assigned. ASCT, autologous stem cell transplant; H-GSS, high genomic scar score; L-GSS, low genomic scar score.

References

    1. Manier S, Salem KZ, Park J, et al. Genomic complexity of multiple myeloma and its clinical implications. Nat Rev Clin Oncol. 2017;14:100–113.
    1. Perrot A, Corre J, Avet-Loiseau H. Risk stratification and targets in multiple myeloma: From genomics to the bedside. Am Soc Clin Oncol Educ Book. 2018;38:675–680.
    1. Robiou du Pont S, Cleynen A, Fontan C, et al. Genomics of multiple myeloma. J Clin Oncol. 2017;35:963–967.
    1. Bolli N, Avet-Loiseau H, Wedge DC, et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat Commun. 2014;5:2997.
    1. Bolli N, Biancon G, Moarii M, et al. Analysis of the genomic landscape of multiple myeloma highlights novel prognostic markers and disease subgroups. Leukemia. 2018;32:2604–2616.
    1. Bolli N, Maura F, Minvielle S, et al. Genomic patterns of progression in smoldering multiple myeloma. Nat Commun. 2018;9:3363.
    1. Walker BA, Mavrommatis K, Wardell CP, et al: Identification of novel mutational drivers reveals oncogene dependencies in multiple myeloma. Blood 132:587-597, 2018 [Erratum: Blood 132:1461, 2018]
    1. Chapman MA, Lawrence MS, Keats JJ, et al. Initial genome sequencing and analysis of multiple myeloma. Nature. 2011;471:467–472.
    1. Khurana E, Fu Y, Chakravarty D, et al. Role of non-coding sequence variants in cancer. Nat Rev Genet. 2016;17:93–108.
    1. Zhang W, Bojorquez-Gomez A, Velez DO, et al. A global transcriptional network connecting noncoding mutations to changes in tumor gene expression. Nat Genet. 2018;50:613–620.
    1. Gloss BS, Dinger ME. Realizing the significance of noncoding functionality in clinical genomics. Exp Mol Med. 2018;50:97.
    1. Piraino SW, Furney SJ. Beyond the exome: the role of non-coding somatic mutations in cancer. Ann Oncol. 2016;27:240–248.
    1. Rahman S, Mansour MR: The role of noncoding mutations in blood cancers. Dis Model Mech 12:dmm041988, 2019.
    1. Zhu H, Uusküla-Reimand L, Isaev K, et al. Candidate cancer driver mutations in distal regulatory elements and long-range chromatin interaction networks. Mol Cell. 2020;77:1307–1321.e10.
    1. Hornshøj H, Nielsen MM, Sinnott-Armstrong NA, et al. Pan-cancer screen for mutations in non-coding elements with conservation and cancer specificity reveals correlations with expression and survival. NPJ Genom Med. 2018;3:1.
    1. Corre J, Cleynen A, Robiou du Pont S, et al. Multiple myeloma clonal evolution in homogeneously treated patients. Leukemia. 2018;32:2636–2647.
    1. Szalat R, Munshi NC: Genomic heterogeneity in multiple myeloma. Curr Opin Genet Dev 30:56-65, 2015 [Erratum: Curr Opin Genet Dev 37:158, 2016]
    1. Aktas Samur A, Minvielle S, Shammas M, et al. Deciphering the chronology of copy number alterations in multiple myeloma. Blood Cancer J. 2019;9:39.
    1. Attal M, Lauwers-Cances V, Hulin C, et al. Lenalidomide, bortezomib, and dexamethasone with transplantation for myeloma. N Engl J Med. 2017;376:1311–1320.
    1. Cibulskis K, Lawrence MS, Carter SL, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013;31:213–219.
    1. Shen R, Seshan VE. FACETS: Allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. Nucleic Acids Res. 2016;44:e131.
    1. Chen X, Schulz-Trieglaff O, Shaw R, et al. Manta: Rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics. 2016;32:1220–1222.
    1. McLaren W, Gil L, Hunt SE, et al. The Ensembl Variant Effect Predictor. Genome Biol. 2016;17:122.
    1. Rosales RA, Drummond RD, Valieris R, et al. signeR: An empirical Bayesian approach to mutational signature discovery. Bioinformatics. 2017;33:8–16.
    1. Sztupinszki Z, Diossy M, Krzystanek M, et al. Migrating the SNP array-based homologous recombination deficiency measures to next generation sequencing data of breast cancer. NPJ Breast Cancer. 2018;4:16.
    1. Hoadley KA, Yau C, Hinoue T, et al: Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell 173:291-304.e6, 2018.
    1. ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium Pan-cancer analysis of whole genomes. Nature. 2020;578:82–93.
    1. Gonzalez-Perez A, Sabarinathan R, Lopez-Bigas N. Local determinants of the mutational landscape of the human genome. Cell. 2019;177:101–114.
    1. Alexandrov LB, Kim J, Haradhvala NJ, et al. The repertoire of mutational signatures in human cancer. Nature. 2020;578:94–101.
    1. Maura F, Petljak M, Lionetti M, et al. Biological and prognostic impact of APOBEC-induced mutations in the spectrum of plasma cell dyscrasias and multiple myeloma cell lines. Leukemia. 2018;32:1044–1048.
    1. Walker BA, Wardell CP, Murison A, et al. APOBEC family mutational signatures are associated with poor prognosis translocations in multiple myeloma. Nat Commun. 2015;6:6997.
    1. Hoang PH, Cornish AJ, Dobbins SE, et al. Mutational processes contributing to the development of multiple myeloma. Blood Cancer J. 2019;9:60.
    1. Keats JJ, Chesi M, Egan JB, et al. Clonal competition with alternating dominance in multiple myeloma. Blood. 2012;120:1067–1076.
    1. Affer M, Chesi M, Chen WG, et al. Promiscuous MYC locus rearrangements hijack enhancers but mostly super-enhancers to dysregulate MYC expression in multiple myeloma. Leukemia. 2014;28:1725–1735.
    1. Maura F, Rustad EH, Yellapantula V, et al. Role of AID in the temporal pattern of acquisition of driver mutations in multiple myeloma. Leukemia. 2020;34:1476–1480.
    1. Walker BA, Mavrommatis K, Wardell CP, et al. A high-risk, Double-Hit, group of newly diagnosed myeloma identified by genomic analysis. Leukemia. 2019;33:159–170.
    1. Palumbo A, Avet-Loiseau H, Oliva S, et al. Revised International Staging System for multiple myeloma: A report from International Myeloma Working Group. J Clin Oncol. 2015;33:2863–2869.
    1. Barwick BG, Neri P, Bahlis NJ, et al. Multiple myeloma immunoglobulin lambda translocations portend poor prognosis. Nat Commun. 2019;10:1911.
    1. Sonneveld P, Avet-Loiseau H, Lonial S, et al. Treatment of multiple myeloma with high-risk cytogenetics: A consensus of the International Myeloma Working Group. Blood. 2016;127:2955–2962.
    1. Mikhael J, Ismaila N, Cheung MC, et al. Treatment of multiple myeloma: ASCO and CCO joint clinical practice guideline. J Clin Oncol. 2019;37:1228–1263.
    1. Perrot A, Lauwers-Cances V, Tournay E, et al. Development and validation of a cytogenetic prognostic index predicting survival in multiple myeloma. J Clin Oncol. 2019;37:1657–1665.
    1. Avet-Loiseau H, Li C, Magrangeas F, et al. Prognostic significance of copy-number alterations in multiple myeloma. J Clin Oncol. 2009;27:4585–4590.
    1. Weinhold N, Kirn D, Seckinger A, et al. Concomitant gain of 1q21 and MYC translocation define a poor prognostic subgroup of hyperdiploid multiple myeloma. Haematologica. 2016;101:e116–e119.
    1. Miller A, Asmann Y, Cattaneo L, et al. High somatic mutation and neoantigen burden are correlated with decreased progression-free survival in multiple myeloma. Blood Cancer J. 2017;7:e612.
    1. Samur MK, Minvielle S, Gulla A, et al. Long intergenic non-coding RNAs have an independent impact on survival in multiple myeloma. Leukemia. 2018;32:2626–2635.
    1. Kuiper R, Broyl A, de Knegt Y, et al: A gene expression signature for high-risk multiple myeloma. Leukemia 26:2406-2413, 2012 [Erratum: Leukemia 28:1178-1180, 2014]
    1. Kumar S, Fonseca R, Ketterling RP, et al: Trisomies in multiple myeloma: Impact on survival in patients with high-risk cytogenetics. Blood 119:2100-2105, 2012 [Erratum: Blood 123:1621, 2014]

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