INTRODUCTION
Multiple myeloma (MM) is consistently preceded by the asymptomatic expansion of clonal plasma cells, termed either monoclonal gammopathy of undetermined significance (MGUS) or smoldering myeloma (SMM). 1 - 5 These two clinical entities were defined over four decades ago based on the size of the aberrant plasma cell clone, and the classification criteria have not been revised since. 6 Unlike other malignancies, the distinction of premalignancy from malignancy across this spectrum of neoplastic plasma cell disorders is not possible with standard morphologic or cytogenetic studies since they all share the same features. As a result, MGUS and SMM have been traditionally differentiated from MM mainly based on clone size and the presence or absence of clinically detectable end-organ damage. This requirement for end-organ damage has been a barrier for progress in the field in terms of early detection and intervention studies. Furthermore, 3%-5% of the adult population has MGUS or SMM, whereas only a subset will ultimately progress to MM, and more accurate methods of risk stratification are needed. 7 - 10
CONTEXT
Key Objective
Is it possible to use genomics to predict which patients with myeloma precursor conditions—monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM)—will remain stable or progress to active multiple myeloma (MM)?
Knowledge Generated
Using genomic data from 374 patients with MGUS or SMM, we introduce the concepts of genomic MM and genomic MGUS to reclassify patients into two biologically distinct groups: a malignant subset with a significant risk of progression to active MM and a truly premalignant group with no genomic evidence of malignancy and no risk of progression. By restricting outcome prediction to patients with genomic MM, we show that integrating genomic features with existing clinical risk stratification models significantly enhances the accuracy of predicting progression.
Relevance (S. Lentzsch)
This study demonstrates that whole-genome and exome sequencing can define biologically distinct subsets of MGUS and SMM, enabling a genomics-based classification with clinical utility. By focusing predictive modeling on truly malignant precursor states, the findings lay the groundwork for refining diagnostic criteria and guiding early intervention in MM.*
*Relevance section written by JCO Associate Editor Suzanne Lentzsch, MD, PhD.
To reduce the need for clinical organ damage as a requirement to diagnose malignant transformation (MM), 11 the International Myeloma Working Group (IMWG) defined a subset of patients with SMM who lack organ damage but show early evidence of emerging disease activity using modern imaging (eg, magnetic resonance imaging [MRI]) and biomarkers (ie, serum free light chains and plasma cell infiltration in the bone marrow) and at imminent risk of progression as MM. 6 This change has allowed clinicians to investigate treatment earlier with the aim of preventing organ damage. Although this and other risk stratification efforts enabled some patients to avoid organ damage and to the development of early intervention strategies for SMM, 12 - 19 it has become even more important to distinguish patients with MGUS and SMM who although asymptomatic have genomic evidence of biological malignancy and are at higher risk of progression compared with those who have an indolent, premalignant condition and can be reassured.
Randomized clinical trials have demonstrated clear benefits in terms of progression-free survival and, more recently, overall survival in persons considered to have high-risk SMM defined mainly using markers of clone size. 16 - 18 A key challenge in these trials lies in the enrollment criteria, which primarily focus on high-risk SMM—a definition that has evolved over time, often before the key trials completed enrollment. 20 , 21 This observation has prompted several research groups to develop prognostic models aimed at identifying patients with SMM at imminent risk of progression who would truly benefit from early intervention. 8 , 22 - 24 While the current gold standard, the IMWG 2/20/20 risk model, 22 successfully identifies patients with a high risk of progression, it has limitations. For instance, some high-risk IMWG 2/20/20 patients may not progress for several years, and identification of these patients can prevent overtreatment in individuals without a clear clinical need or immediate risk of organ damage. On the other hand, patients classified as not having high-risk SMM may be at risk of early progression and need to be identified for closer follow-up.
Since MM progression is driven at least partly by the accumulation and selection of distinct genomic alterations, several studies have explored the role of genomic profiling in distinguishing progressive from stable MGUS and SMM. 25 - 31 Although often limited in sample size and being primarily focused on SMM, these studies have identified specific genomic events linked to progression risk such as MYC translocations and RAS mutations. 25 - 31 This finding underscores the potential of genomic profiling to differentiate clonal entities that have undergone malignant transformation from those that are not independent of traditional markers of disease burden. However, previous studies have consistently analyzed MGUS and SMM as separate entities, and the use of genomics to refine this historical classification has never been explored.
The aim of this study is to leverage comprehensive genomic profiling to redefine myeloma precursor conditions, and this, in turn, will improve risk prediction for progression to MM. To do so, we assembled a large training and validation set of patients with SMM and MGUS with available comprehensive genomic profiling to develop a robust classification model that distinguishes patients with evidence of malignant transformation in the clonal plasma cells from those without. Next, we identified distinct myeloma genomic defining events associated with progression in patients with evidence of malignant transformation to MM. By integrating genomic and clinical data, we show that genomic profiling significantly improves the predictive accuracy of the current gold standard—the IMWG 2/20/20 prognostic model—and helps identify patients whose disease is already progressing. These findings underscore the critical role of genomic profiling in redefining myeloma precursor conditions, with important implications for future translational research in this field.
METHODS
A total of 374 patients with SMM (n = 290) or MGUS (n = 84) with available whole-exome sequencing (WES, n = 190) or whole-genome sequencing (WGS, n = 184) were included in the study (Data Supplement, Tables S1 and S2, online only). All sequencing FASTQ files were aligned to Genome Reference 38, centralized, and analyzed using the Myeloma Genome Project pipeline (Data Supplement). All tumor samples were sorted using either CD138+ magnetic beads or flow cytometry. All tumor samples were analyzed using as germline match normal, either peripheral blood mononuclear cells or CD138-negative bone marrow cells. Careful genomic quality control was performed in each patient by integrating copy number variants (CNVs), single-nucleotide variants, the presence of oncogenic rearrangement, and/or productive V(D)J in the immunoglobulin regions to confirm the presence of a clonal B-cell population and assess for potential contamination of normal cells into the tumor samples. The analytical workflows used for this study are available in the Data Supplement (Fig S1) and in github.
RESULTS
Study Population
Among the 374 patients with high-quality sequencing data, 62 (16.5%) were enrolled in SMM early intervention clinical trials. 12 , 15 An additional 11 (3%) did not have complete follow-up. These patients were only included in the classification part of the study and not included in the time-to-event analysis (Data Supplement, Fig S1). A total of 58% and 78% of patients with MGUS and SMM, respectively, had a radiologic evaluation to exclude bone lesions at the time of the bone marrow biopsy with X-ray, positron emission tomography-computed tomography, or MRI. Overall, none of the patients had evidence of SLiM or CRAB criteria at the time of the sampling. 6
A total of 301 patients (81 MGUS and 220 SMM) were followed over time and treated only in the case of organ damage or SLiM criteria. 6 , 11 Disease progression was defined in accordance with IMWG SLiM-CRAB diagnostic criteria for MM. 6 Time to progression into MM was defined from the time of sample collection to the last follow-up or progression. As expected, patients with SMM experienced more and earlier progressions to MM than MGUS ( P < .0001; Fig 1 A). Overall, after a median follow-up of 46 months, 80 of 220 (36%) patients with SMM and 4 of 81 (5%) patients with MGUS experienced progression ( P < .0001). The key clinical features for patients with MGUS and SMM are summarized in the Data Supplement (Table S1).
Based on the proportion of SMM and MGUS patients with WES or WGS, all eligible patients from Mayo Clinic and the MDACC were included as the validation set (n = 97). The remaining patients were assigned to the training set (n = 277, 68 of which were either lost to follow-up or enrolled on an interventional clinical trial). In the training and validation set, data to calculate risk of progression into MM from SMM according to the IMWG 2/20/20 model were available for 161 (76.5%) and 74 (76.2%) patients, respectively. In both data sets, IMWG 2/20/20 was able to identify SMM patients with significantly shorter time to progression (Figs 1 B and 1 C). However, differences in clinical outcomes between IMWG 2/20/20 low- and intermediate-risk groups were observed only in the validation set. Overall, the IMWG 2/20/20 c-index to predict SMM progression in the training and validation set was 0.67 (0.63-0.71) and 0.72 (0.68-0.76), respectively (Fig 1 D). Recently proposed clinical markers for predicting SMM progression, such as hemoglobin and creatinine, did not show any independent prognostic value in predicting the risk of progression in either the training or validation cohort (Fig 1 E). 24
Genomic Profiling to Define Malignant Transformation
By integrating CNV and single base substitution (SBS)/indel data from 1,933 newly diagnosed MM (NDMM) patients, a catalog of 134 myeloma genomic defining events was recently proposed. 34 Leveraging this model, we investigated the prevalence and distribution of these 134 events among SMM and MGUS patients and their association with progression into MM in both the training and validation cohorts (Data Supplement, Fig S2). Given the distinct clinical setting, a few modifications were implemented from the original model. Specifically, in the setting of NDMM, CNV signatures were previously used to predict for the prevalence of chromothripsis. 35 , 36 However, because chromothripsis is infrequent, between SMM and MGUS, we decided to reanalyze de novo CNV signatures, identifying five distinct CNV-signatures (Data Supplement, Fig S3). Three signatures were characterized by multiple aneuploidies—including focal events—and enriched in SMM versus MGUS and in patients who progressed (SMM CNV SIG 1, 3, and 5). The remaining signatures were marked by minimal or absent CNVs and was associated with MGUS and SMM patients that remained stable for decades (SMM CNV SIG 2 and 5).
Regarding mutational signatures, in NDMM, most patients show evidence of aberrant apolipoprotein B mRNA editing enzyme (APOBEC) activity and the focus is typically on identifying those with higher levels as this is prognostic (ie, hyper-APOBEC). 34 , 37 In the context of myeloma precursor conditions, we instead focused on the APOBEC presence versus absence, given that APOBEC-mediated mutagenesis is a known driver of malignant transformation in solid tumors. 38 - 42 To achieve this, APOBEC SBS signatures (SBS2 and SBS13) were assessed only in patients with an overall mutational burden >50 SBS (Data Supplement, Fig S4). 43
Integrating the above data, a total of 28 myeloma genomic defining events were identified to be associated with progression to MM in the training set either considering all patients and limited to SMM (Fig 2 ; Data Supplement, Tables S3, S4, and S5). This list included the presence of APOBEC mutagenesis, t(4;14) ( NSD2 ; IGH ), RAS mutations, hyperdiploidy (HY), and MYC translocations. Interestingly, we observed a group of patients in both the training and validation sets who did not exhibit any of these events and were characterized by an extremely indolent genomic profile (Fig 2 ). The mutational and CNV profiles of these patients were remarkably similar to those observed in normal B cells, 42 suggesting that the malignant transformation has not yet occurred in these patients—and may never occur. By contrast, the other patients exhibited varying degrees of genomic complexity and aberrations consistent with myeloma genomic defining events. 34 In fact, the genomic profile of most of these patients in both the training and validation sets closely resembled, or was identical to, the profiles observed across more than 2,000 patients with NDMM. 34 This suggests that this group includes patients in whom malignant transformation to MM has already occurred at the genomic level, although clonal expansion leading to organ damage has not yet taken place. Based on these data and 28 myeloma genomic defining events associated with progression, we used the training set to develop a workflow that differentiates MGUS and SMM into two genomically distinct entities: one with evidence of malignant transformation (ie, genomic MM) and one without (ie, genomic MGUS; Fig 3 A; Data Supplement, Figs S5 and S6 and Tables S6 and S7). Overall, 39% of MGUS and 94% of SMM patients had genomic evidence of malignant transformation in the training set (ie, genomic MM). Importantly, none of the patients with genomic MGUS experienced progression into MM in the training set (Fig 3 B). When we applied the same workflow and classification to the validation set, 47% of MGUS and 85% of SMM had evidence of malignant transformation. Importantly, also in the validation set, none of the genomic MGUS experienced progression (Fig 3 C). All patients with IMWG 2/20/20 high-risk SMM were classified as genomic MM in both the training and validation set (Fig 3 A). Interestingly, 5 of 62 (8%) patients enrolled in early intervention trials for high-risk SMM were genomically classified as genomic MGUS. Overall, these data demonstrated that the MM genomic background and malignant transformation can be acquired very early in time, even before the clinical SMM phase as defined currently 6 (eg, Figs 3 D- 3 F).
Myeloma Genomic Defining Events Associated With SMM Progression
Because of the comprehensive genomic resolution and the workflow described above, we identified patients in whom malignant transformation had already occurred (ie, genomic MM), despite clinical parameters indicating early and asymptomatic disease. By contrast, the other patients (ie, genomic MGUS) neither experienced clinical progression—regardless of their SMM or MGUS status—nor exhibited any evidence of malignant transformation. Therefore, it is essential to exclude the latter patients from any prognostic modeling as their inclusion would distort and overfit the prognostic impact of different myeloma genomic defining events, which are, by definition, enriched in the genomic MM group compared with the genomic MGUS group. In fact, distinct genomic drivers may predict malignant transformation but not necessarily the risk of progression to MM once transformation has occurred. Among the patients with genomic MM in the training set who were not enrolled in early intervention clinical trials (n = 158), 55 (35%) experienced progression and 29 (17%) remained stable for more than 5 years, despite having a myeloma-transformed genomic profile (Figs 3 B- 3 F).
To identify the genomic features underlying this clinical heterogeneity, we investigated the prognostic impact of each myeloma genomic defining event in predicting MM progression in the training set. After running 5-fold cross-validations corrected for IMWG 2/20/20, only the presence of RAS mutations, genomic events involving MYC , the presence of neoplastic CNV signatures, and APOBEC mutagenesis were predictive of early progression in at least two cross-validations (Figs 4 A- 4 D; Data Supplement, Fig S7). Patients with at least two of these four genomic events had rapid progression in both the training and the validation set (Data Supplement, Fig S8). Adding the presence of high-risk myeloma genomic defining events into the IMWG 2/20/20 (ie, genomic IMWG; Data Supplement, Figs S9 and S10; Fig 5 A) significantly improves the accuracy in predicting MM progression in both the training (c-index 0.69 v 0.74) and validation set (c-index 0.74 v 0.79; Figs 5 B- 5 E). Using the genomic IMWG model developed in the training set to create a weighted risk score by estimated log high risk in the validation set showed a c-index of 0.77 (0.73-0.81; Data Supplement). Finally, restricting the analysis to the patients with radiologic assessment at the time of the biopsy confirmed the accuracy improvement of the genomic IMWG model (Data Supplement, Fig S11). Overall, these data show that comprehensive genomic profiling can improve the accuracy of predicting progression to MM in SMM patients with genomic MM.
DISCUSSION
Unlike other malignancies, it is not possible using standard histopathology to differentiate malignancy from premalignancy in clonal plasma cell disorders, necessitating the use of crude tumor burden and clinical parameters such as end-organ damage to define MM, and to distinguish it from MGUS and SMM. 6 Yet, given the high prevalence of plasma cell dyscrasias in the general population 10 and the benefit of early intervention in high-risk SMM, 16 there is a compelling need to redefine the risk stratification in such asymptomatic conditions.
To our knowledge, our study shows for the first time that 90% of SMM, including all patients with high-risk SMM, has malignancy signatures indistinguishable from MM. 34 By introducing the concept of distinguishing genomic MGUS from genomic MM, we propose a new genomic-based classification model that significantly improves the one developed by Kyle et al 3 decades ago. 2 , 8 In solid tumors, it is well known that in situ carcinomas and cancerous cells, such as those found and characterized in Barrett esophagus, exhibit multiple genomic alterations commonly found in cancer, despite remaining quiescent for years. 44 , 45 Through various orthogonal approaches, we now understand that most tumors, including MM, develop over decades, with the early acquisition of genomic events that likely initiate neoplastic transformation. 9 , 46 - 49 Afterward, the tumor expands following classical Darwinian evolution, with patterns of neutrality and selection. 4 , 50 , 51 Our study shows that this pattern is present in clonal plasma cell disorders and that evidence of malignant transformation can be detected in most SMM patients and a fraction of MGUS. This finding confirms our hypothesis based on the well-established clinical course of these disorders and the fact that the absence of symptoms or end-organ damage does not mean that these are entirely benign or premalignant states.
Our findings have major clinical implications beyond the biological distinction of premalignancy and malignancy. The identification of patients with SMM and MGUS who will progress and experience organ damage has been a key priority in the MM community for decades. With the advent of novel and highly effective immunotherapies that have shown clear advantages in SMM, 16 , 17 the need for more accurate models has become apparent—to better identify patients at imminent risk while preventing unnecessary exposure to potential side effects in those with indolent disease. Our study leads to an important shift in our understanding, defining genomic MM as the equivalent of malignancy but without evidence of end-organ damage that has traditionally been a requirement to define MM in clinical practice, the equivalent of stage I-II chronic lymphocytic leukemia or asymptomatic low-grade follicular lymphoma. 52 , 53 This is of particular importance for SMM where we find that 90% has evidence of malignant transformation and that our proposed genomic markers and classification can further refine the risk stratification of SMM and enable us to identify with greater accuracy candidates for early intervention. An important caveat is that although 40% of the MGUS patients in our study showed evidence of malignant transformation and would be considered genomic MM, this is likely a significant overestimation as bone marrow biopsies are not recommended in low-risk MGUS and since patients with MGUS followed in large cancer centers—such as those involved in this study—tend to have a higher disease burden and risk compared with MGUS diagnosed in the community among the general population. 54 , 55 Furthermore, this high proportion can also be explained by the exclusion of MGUS with significantly more polyclonal than clonal bone marrow plasma cells, whose genomics is not accessible by bulk sequencing of CD138-selected cells because of the lack of the dominant clonal population. Importantly, because of the resolution limitations of bulk sequencing, we cannot rule out the presence of minor subclones harboring transformation-associated alterations that remain undetectable at the time of sampling or are located in anatomic sites not captured by standard bone marrow biopsies. While the 5-year risk of progression in genomic MGUS is zero in both the training and validation set, the possibility of transformation over longer periods cannot be entirely excluded—especially considering that MM may take 30-40 years to fully develop. 48 , 49 This underscores the importance of integrating genomic and clinical variables to guide appropriate follow-up strategies for patients with myeloma precursor conditions.
Interestingly, some patients with genomic MM remain stable for years despite having a genomic profile similar to NDMM (Figs 3 D- 3 F). This behavior could be explained by extrinsic factors such as immune control, whose dysregulation over time because of intrinsic or extrinsic factors might let the MM clone expand and cause organ damage. Evidence for immune-mediated control has previously been demonstrated in xenograft models, where human MGUS-derived cells exhibited progressive growth in mice—suggesting that the clinical stability of these lesions may partly result from growth restraints imposed by the immune system rather than intrinsic tumor cell properties. 56 In contrast to premalignant solid tumors or carcinoma in situ, which may be excised, MM-transformed clones are not easily eradicated, even with intense therapies. 12 , 21 , 57 Therefore, strategies aimed at maximizing the pre-existing immune control to keep these neoplastic clones in check and limiting the selection of more aggressive clones are attractive and have already shown some advantages 16 , 17 although further genomic and integrated immune studies are needed. Other early intervention strategies with more aggressive approaches might be more effective on killing the aberrant plasma cell, but they might also irreversibly affect this immune control.
Importantly, the genomic classification proposed here is not primarily designed for prognostication, but rather to distinguish two biologically distinct entities—one of which follows an indolent and likely benign clinical course. Including these patients, who typically lack myeloma genomic defining events and never progress, in analyses with genomic MM may dilute associations or lead to overfitting in prognostic models built on key genomic features. Therefore, to accurately assess the risk of progression in myeloma precursor conditions, we should focus solely on the biological subset that is at genuine risk of progression: genomic MM (Figs 5 - 6 ). In fact, while some of these patients remain stable for years, others exhibit genomic profiles that not only resemble those of MM but also suggest a high risk of imminent or ongoing progression. In line with previous evidence, 25 , 27 , 29 - 31 , 58 our data suggest that these alterations associated with rapid progression reflect activation of MYC and RAS pathways and genomic instability (ie, CNV signatures and APOBEC mutagenesis). It is currently unclear which approach—myeloma-like treatment or immune control—would be most effective for these patients, and multiple ongoing clinical trials might provide soon the answer. In the recent AQUILA randomized phase III clinical trial, 20% of patients with SMM progressed within a few months of initiating daratumumab treatment. 16 , 59 This subgroup likely represents patients in whom immune surveillance mechanisms are ineffective, potentially corresponding to the high-risk group of genomic MM reported here. Confirming these findings in future studies will be essential as these patients may require early initiation of multiagent antimyeloma therapy.
While the results of different trials will be released in the coming years, it is important to highlight that combining genomic studies in SMM and MGUS with traditional IMWG 2/20/20 risk stratification will be critical for the interpretation of clinical trial results. Since almost all patients with SMM (90%) have genomic MM, genomics alone is insufficient to identify patients at imminent risk of progression who may benefit from early intervention. On the other hand, since there are some patients with SMM (and the majority with MGUS) who do not have genomic MM, we risk overtreating patients who might have genomic MGUS. Therefore, we recommend combining genomic studies to distinguish genomic MM versus genomic MGUS along with IMWG 2/20/20 (ie, genomic IMWG 2/20/20) or other clinical risk stratification models to identify patients for clinical trials and patients who will benefit the most from early intervention strategies. As all IMWG 2/20/20 high-risk patients were genomic MM in both the training and validation sets, we believe that, despite its limitations, this model should be use as enrollment criteria when more comprehensive genomic profiling strategies are not available, thus providing an accessible and reproducible way to select patients and interpret trials. Furthermore, dynamic models that track changes in disease burden over time are particularly promising 24 as these markers are easily accessible through peripheral blood and highly sensitive in predicting ongoing progression to MM. Unfortunately, such data were not available for this study, making integration of dynamic changes with genomics unfeasible at this time.
While our study did not include longitudinal samples from the same individuals, previous research has demonstrated that SMM can progress to MM through either branching or linear evolutionary trajectories. In both models, the majority of genomic driver mutations are shared between the precursor and transformed stages, 25 - 27 , 60 supporting the concept of early transformation as a fundamental feature to identify the patients at risk of progression.
In conclusion, by combining clinical and genomic data from patients with SMM and MGUS, to our knowledge, we leveraged comprehensive genomic profiling to provide for the first time a genomic classification of MGUS and SMM (Fig 6 ). Based on our study, over 40% of MGUS and approximately 10% of SMM do not have evidence of neoplastic transformation (ie, genomic MGUS), with a need for surveillance rather than intervention. On the other hand, 90% of SMM has genomic malignancy (genomic MM), and within this group, progression to end-organ damage or lack thereof may be dependent to some extent on tumor burden (as estimated by models such as IMWG 2/20/20), immune control, and occurrence and accumulation of other high-risk genomic events. The rapid decrease in sequencing cost and the integration of genomics into clinical practice will offer unprecedented opportunities for clinicians to better assess patients' diagnosis and risk of progression, ultimately improving patients' quality of life and clinical outcomes.
ACKNOWLEDGMENT
The authors sincerely thank our patients and their families for donating their samples for research purposes.
Francesco Maura
Honoraria: Sanofi, Medidata
P. Leif Bergsagel
Consulting or Advisory Role: CellCentric, Abbvie, Omeros, Pfizer
Research Funding: Pfizer (I), Celgene, Sanofi
Patents, Royalties, Other Intellectual Property: Mouse model of multiple myeloma
Open Payments Link: https://openpaymentsdata.cms.gov/physician/59882
Shaji Kumar
Consulting or Advisory Role: Takeda (Inst), Janssen Oncology (Inst), Genentech/Roche (Inst), Abbvie (Inst), Bristol Myers Squibb/Celgene (Inst), Pfizer (Inst), Regeneron (Inst), Sanofi (Inst), K36 (Inst)
Research Funding: Takeda (Inst), Abbvie (Inst), Novartis (Inst), Sanofi (Inst), Janssen Oncology (Inst), MedImmune (Inst), Roche/Genentech (Inst), CARsgen Therapeutics (Inst), Allogene Therapeutics (Inst), GlaxoSmithKline (Inst), Regeneron (Inst), Bristol Myers Squibb/Celgene (Inst)
Travel, Accommodations, Expenses: Abbvie, Pfizer, Janssen, Beigene
Andriy Derkach
This author is a member of the Journal of Clinical Oncology Editorial Board. Journal policy recused the author from having any role in the peer review of this manuscript.
Esteban Braggio
Consulting or Advisory Role: DASA
Patents, Royalties, Other Intellectual Property: Establishment of a cell line from a patient with Waldenstrom Macroglobulinemia
Yan Asmann
Employment: Mayo Clinic
Benjamin T. Diamond
Honoraria: Sanofi, Janssen Research & Development
Consulting or Advisory Role: Sanofi, Janssen Oncology
Malin Hultcrantz
Consulting or Advisory Role: Curio Science, Bristol Myers Squibb/Celgene, GlaxoSmithKline, Intellisphere, Projects in Knowledge, Janssen Oncology
Research Funding: Daiichi Sankyo (Inst), Amgen (Inst), GlaxoSmithKline (Inst), Bristol Myers Squibb/Celgene, Abbvie, BeiGene, Janssen, SpringWorks Therapeutics
Matteo Claudio Da Vià
Honoraria: Johnson & Johnson/Janssen, Sanofi
Consulting or Advisory Role: Johnson & Johnson/Janssen, Pfizer, Menarini, Sanofi, Amgen
Speakers' Bureau: Johnson & Johnson/Janssen, Pfizer
Research Funding: Pfizer, Johnson & Johnson/Janssen
Travel, Accommodations, Expenses: Johnson & Johnson/Janssen, Amgen
Claudio de Magistris
Expert Testimony: CSL Behring
Danny DeAvila
Employment: Moffitt
Rafael Renatino Canevarolo
Research Funding: Abbvie
Patents, Royalties, Other Intellectual Property: I have a patent for “A model of clinical synergy in cancer, PCT/US2020/062232 (WO/2021/108551-A1)” pending, a patent for “A multiomic approach to modeling of gene regulatory networks in multiple myeloma, PCT/US2022/024217 (WO/2022/217136-A1)” pending, and a patent for “Altering epigenetic landscapes control progression and refractory disease states in multiple myeloma, PCT/US2023/078667 (WO/2024/097981)” pending
Jamie Teer
Employment: H. Lee Moffitt Cancer Center and Research Institute
Research Funding: Turnstone Bio (Inst)
Patents, Royalties, Other Intellectual Property: Patent application: Large Data Set Negative Information Storage Model
Ariosto S. Silva
Research Funding: Abbvie, Karyopharm Therapeutics
Patents, Royalties, Other Intellectual Property: U.S. Patent Application #63/173,389 SELINEXOR SYNERGISM IN MULTIPLE MYELOMA, U.S. Patent Application #63/301,507 A MULTIOMIC APPROACH TO MODELING OF GENE REGULATORY NETWORKS IN MULTIPLE MYELOMA
Rachid Baz
Honoraria: Hikma Pharmaceuticals
Consulting or Advisory Role: BMS, Sanofi, Janssen, Pfizer
Research Funding: Karyopharm Therapeutics (Inst), Abbvie (Inst), Janssen (Inst), BMS (Inst), Daiichi Sankyo/Lilly (I), Seagen (I), Regeneron (I), Zymeworks (I), Arvinas (I), Marker Therapeutics (I), Pionyr (I), Quantum Leap Healthcare Collaborative (I)
Other Relationship: GlaxoSmithKline
Krina K. Patel
Leadership: Bristol Myers Squibb/Celgene, oricel, Kite, a Gilead company
Consulting or Advisory Role: Celgene, Bristol Myers Squibb, Janssen, Pfizer, Merck, Takeda, Abbvie, AstraZeneca, Legend Biotech, Novartis, Regeneron
Research Funding: Celgene/Bristol Myers Squibb, Janssen, Nektar, Abbvie/Genentech, Indapta Therapeutics
Travel, Accommodations, Expenses: Bristol Myers Squibb
Peter Campbell
Employment: Quotient Therapeutics Ltd
Stock and Other Ownership Interests: Quotient Therapeutics Ltd
Consulting or Advisory Role: Quotient Therapeutics
Gareth Morgan
Honoraria: BMS, Janssen, Genentech, Sanofi, Takeda, GlaxoSmithKline, Regeneron
Consulting or Advisory Role: Takeda, Takeda, GlaxoSmithKline
Travel, Accommodations, Expenses: BMS, Janssen
Rafael Fonseca
Employment: Mayo Clinic, Mayo Clinic
Leadership: Antengene
Stock and Other Ownership Interests: Caris Life Sciences
Honoraria: Celgene, Bristol Myers Squibb, Bayer, Amgen, Janssen, Kite, a Gilead company, Merck Sharp & Dohme, Juno Therapeutics, Takeda, Abbvie, Aduro Biotech, Sanofi, GlaxoSmithKline, Pfizer, Regeneron, RA Capital
Consulting or Advisory Role: Celgene, Bristol Myers Squibb, Bayer, Amgen, Janssen, Abbvie, Kite, a Gilead company, Merck Sharp & Dohme, Juno Therapeutics, Takeda, Aduro Biotech, Sanofi, OncoTracker, GlaxoSmithKline, Karyopharm Therapeutics, Oncopeptides, Pfizer, Pharmacyclics, Regeneron, RA Capital, Adaptive Biotechnologies
Patents, Royalties, Other Intellectual Property: Dr Fonseca has received a patent for the prognostication of MM based on genetic categorization of the disease
Travel, Accommodations, Expenses: Celgene, Bristol Myers Squibb, Bayer, Amgen, Janssen, Kite, a Gilead company, Merck Sharp & Dohme, Juno Therapeutics, Takeda, Abbvie, Aduro Biotech, Sanofi, OncoTracker
Other Relationship: Adaptive Biotechnologies, Caris Life Sciences
Ola Landgren
Honoraria: Celgene, Bristol Myers Squibb, Medscape, Amgen, Janssen, Karyopharm Therapeutics, GlaxoSmithKline
Consulting or Advisory Role: Bristol Myers Squibb, Celgene, Janssen, GlaxoSmithKline, Pfizer
Research Funding: Amgen (Inst), Janssen (Inst), Pfizer (Inst), Menarini Silicon Biosystems (Inst), Sebia (Inst)
Robert Z. Orlowski
Stock and Other Ownership Interests: Asylia Therapeutics, Inc
Consulting or Advisory Role: Bristol Myers Squibb, Janssen, Sanofi, Takeda, Regeneron, GlaxoSmithKline, BioTheryX, Meridian Therapeutics, Monte Rosa Therapeutics, IASO Biotherapeutics, Lytica Therapeutics
Research Funding: Heidelberg Pharma (Inst), Asylia Therapeutics, Inc (Inst), CARsgen Therapeutics (Inst), Sanofi/Aventis (Inst), Exelixis (Inst), Takeda (Inst), Janssen Biotech (Inst), Celgene/Bristol Myers Squibb (Inst)
Patents, Royalties, Other Intellectual Property: Asylia Therapeutics, Inc
Kenneth H. Shain
Honoraria: Bristol Myers Squibb (Celgene), Janssen Oncology, Amgen, GlaxoSmithKline, Sanofi, Abbvie, Karyopharm Therapeutics, Sebia
Research Funding: Pfizer, Abbvie, Karyopharm Therapeutics
Niccolo Bolli
Honoraria: Janssen, GlaxoSmithKline, Pfizer, Jazz Pharmaceuticals, Amgen, Sanofi, Menarini, Oncopeptides
Consulting or Advisory Role: Pfizer, Takeda, Janssen, GlaxoSmithKline
Research Funding: Janssen
Travel, Accommodations, Expenses: Janssen
Saad Usmani
Consulting or Advisory Role: Janssen Oncology, GlaxoSmithKline, Abbvie, Genentech, Bristol Myers Squibb/Celgene, Regeneron, Oricell Therapeutics, AstraZeneca
Research Funding: Janssen Oncology, Bristol Myers Squibb, GlaxoSmithKline, Gracell Therapeutics, Gilead Sciences, K36 Therapeutics
S. Vincent Rajkumar
Honoraria: Research to Practice, Medscape
Patents, Royalties, Other Intellectual Property: Authorship Royalties from Up To Date
No other potential conflicts of interest were reported.
See accompanying Bridging the Gap, p. 135
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The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
SUPPORT
Supported by the Sylvester Comprehensive Cancer Center NCI Core Grant (P30 CA 240139) and the Memorial Sloan Kettering Cancer Center National Cancer Institute (NCI) Core Grant (P30 CA 008748). The research reported in this publication was also supported by the Mayo Clinic Hematological Malignancies Program. This research was partly supported by generous Paula and Roger Riney Foundation funding. Finally, this research was partly supported by generous funding from additional philanthropic donations to the Mayo Clinic and NCI U01CA271410 grant. F.M. and A. McPherson are supported by NIH-NCI R37CA289752-01A1. S.V.R. is supported by R01 CA 168762, U01 CA271410, and P50CA186781. K.M. has received funding from the Multiple Myeloma Research Foundation, The American Society of Hematology, and the International Myeloma Society. G.M. and P.L.B. are supported by the Myeloma Solutions Fund, the Paula & Rodger Riney Foundation, the Ammon Foundation, the National Cancer Institute (NCI) Cancer Center Support Grant P30CA016087 of the Laura and Isaac Perlmutter Cancer Center, and NCI Cancer Cause and Prevention Research Grants R01CA249981 and R01CA300518. Moffitt: This research was made possible through the Oncology Research Information Exchange Network (ORIEN) Avatar Project in collaboration with Aster Insights (formerly known as M2Gen), the Total Cancer Care protocol, and supported by the NTRO, Tissue Core, Molecular Genomics, Cancer Pharmacokinetics and Pharmacodynamics, Biostatistics and Bioinformatics, and Collaborative Data Services shared resource facilities at the H. Lee Moffitt Cancer Center & Research Institute, an NCI-designated Comprehensive Cancer Center (P30-CA076292). We are also grateful for the service of the Cores of Aster Insights. This work was supported by philanthropic support from the Pentecost Family Myeloma Research Center and the Bowling Family Foundation. O.L. is supported by NCI R01 CA 248398, NCI P30 CA 240139, the Riney Family Multiple Myeloma Research Program Fund, Tow Foundation, Myeloma Solutions Fund, and Cannon Guzy Family Fund. N.B. was supported by the European Research Council under the European Union's Horizon 2020 research and innovation program (grant agreement Nos 817997 and 101123230), by the International Myeloma Society (IMS) and Paula and Rodger Riney Foundation Translational Research Grant Application – 2023. B.T.D. is supported by the NCI under Award No. K12CA226330. R.Z.O., the Florence Maude Thomas Cancer Research Professor, and K.K.P. would like to acknowledge support from the Dr Miriam and Sheldon G. Adelson Medical Research Foundation, the Riney Family Multiple Myeloma Research Fund at MD Anderson from the Paula and Rodger Riney Foundation, the Myeloma Solutions Fund, the Leukemia & Lymphoma Society (SCOR-12206-17), the MD Anderson Cancer Center High Risk Multiple Myeloma Moon Shot, the Brock Family Myeloma Research Fund, the Yates Ortiz Myeloma Fund, and the Diane & John Grace Family Foundation.
F.M., P.L.B., B.Z., and S.K. are cofirst authors. O.L., R.Z.O., K.H.S., N.B., S.U., and S.V.R. cosenior authors.
DATA SHARING STATEMENT
A data sharing statement provided by the authors is available with this article at DOI https://doi.org/10.1200/JCO-25-01733 . The previously published WGS and WES data are available on EGA: EGAD00001006363, EGAD00001003309, and EGAS00001004467, and dbGap: phs001323.v3.p1. The MSKCC WGS data are available through the International Cancer Genome Consortium ( https://platform.icgc-argo.org/ ), were partially generated by the Polyethnic 1000 initiative, and are currently being uploaded on EGA. The Mayo Clinic WGS and WES are available under GSE: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1301307 . The University of Milan WGS data are available on GEO under the accession code: PRJNA1305657. The MDACC genomic data are available in EGA under EGAC50000000282. The molecular data generated at the Moffitt Cancer Center used in this research were generated through private funding by Aster Insights ( www.asterinsights.com ) in collaboration with the Oncology Research Information Exchange Network (ORIEN, www.oriencancer.org ). Requests for access to the raw data (FASTQ/BAM) used in this study can be submitted here at https://researchdatarequest.orienavatar.com/ . The analytical workflows used for this study are available in github: https://github.com/bachisiozic/CNV_mmsig and https://github.com/bachisiozic/SMM_functions .
AUTHOR CONTRIBUTIONS
Conception and design: Francesco Maura, P. Leif Bergsagel, Shaji Kumar, Andriy Derkach, Gareth Morgan, Rafael Fonseca, Ola Landgren, Robert Z. Orlowski, Kenneth H. Shain, Saad Usmani, S. Vincent Rajkumar
Financial support: Francesco Maura, P. Leif Bergsagel, Ola Landgren, Kenneth H. Shain, Niccolo Bolli, Saad Usmani
Administrative support: Francesco Maura, Yan Asmann, Gareth Morgan, Ola Landgren, Kenneth H. Shain, S. Vincent Rajkumar
Provision of study materials or patients: Francesco Maura, P. Leif Bergsagel, Kylee Maclachlan, Giancarlo Castellano, Danny DeAvila, Erin M. Siegel, Patrick Blaney, Rachid Baz, Gareth Morgan, Rafael Fonseca, Ola Landgren, Robert Z. Orlowski, Kenneth H. Shain, Niccolo Bolli, Saad Usmani, S. Vincent Rajkumar
Collection and assembly of data: Francesco Maura, P. Leif Bergsagel, Shaji Kumar, Kylee Maclachlan, Esteban Braggio, Yan Asmann, Benjamin T. Diamond, Akihiro Maeda, Antonio Matera, Stefania Pioggia, Matteo Claudio Da Vià, Claudio de Magistris, Daniel Leongamornlert, Praneeth Reddy Sudalagunta, Erin M. Siegel, Phaedra Agius, Jamie Teer, Yusuke Yamashita, Ariosto S. Silva, Patrick Blaney, Rachid Baz, Krina K. Patel, Gareth Morgan, Rafael Fonseca, Ola Landgren, Robert Z. Orlowski, Kenneth H. Shain, Niccolo Bolli, Saad Usmani, S. Vincent Rajkumar
Data analysis and interpretation: Francesco Maura, P. Leif Bergsagel, Bachisio Ziccheddu, Shaji Kumar, Andriy Derkach, Juan-Jose Garces, Ross Firestone, Michael Durante, Benjamin T. Diamond, Marios Papadimitriou, Malin Hultcrantz, Alessio Marella, Giancarlo Castellano, Marta Lionetti, Danny DeAvila, Rafael Renatino Canevarolo, Andrew McPherson, Peter Campbell, Gareth Morgan, Rafael Fonseca, Ola Landgren, Robert Z. Orlowski, Kenneth H. Shain, Niccolo Bolli, Saad Usmani, S. Vincent Rajkumar
Manuscript writing: All authors
Final approval of manuscript: All authors
Accountable for all aspects of the work: All authors
AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
Genomics Define Malignant Transformation in Myeloma Precursor Conditions
The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/jco/authors/author-center .
Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians ( Open Payments ).
Francesco Maura
Honoraria: Sanofi, Medidata
P. Leif Bergsagel
Consulting or Advisory Role: CellCentric, Abbvie, Omeros, Pfizer
Research Funding: Pfizer (I), Celgene, Sanofi
Patents, Royalties, Other Intellectual Property: Mouse model of multiple myeloma
Open Payments Link: https://openpaymentsdata.cms.gov/physician/59882
Shaji Kumar
Consulting or Advisory Role: Takeda (Inst), Janssen Oncology (Inst), Genentech/Roche (Inst), Abbvie (Inst), Bristol Myers Squibb/Celgene (Inst), Pfizer (Inst), Regeneron (Inst), Sanofi (Inst), K36 (Inst)
Research Funding: Takeda (Inst), Abbvie (Inst), Novartis (Inst), Sanofi (Inst), Janssen Oncology (Inst), MedImmune (Inst), Roche/Genentech (Inst), CARsgen Therapeutics (Inst), Allogene Therapeutics (Inst), GlaxoSmithKline (Inst), Regeneron (Inst), Bristol Myers Squibb/Celgene (Inst)
Travel, Accommodations, Expenses: Abbvie, Pfizer, Janssen, Beigene
Andriy Derkach
This author is a member of the Journal of Clinical Oncology Editorial Board. Journal policy recused the author from having any role in the peer review of this manuscript.
Esteban Braggio
Consulting or Advisory Role: DASA
Patents, Royalties, Other Intellectual Property: Establishment of a cell line from a patient with Waldenstrom Macroglobulinemia
Yan Asmann
Employment: Mayo Clinic
Benjamin T. Diamond
Honoraria: Sanofi, Janssen Research & Development
Consulting or Advisory Role: Sanofi, Janssen Oncology
Malin Hultcrantz
Consulting or Advisory Role: Curio Science, Bristol Myers Squibb/Celgene, GlaxoSmithKline, Intellisphere, Projects in Knowledge, Janssen Oncology
Research Funding: Daiichi Sankyo (Inst), Amgen (Inst), GlaxoSmithKline (Inst), Bristol Myers Squibb/Celgene, Abbvie, BeiGene, Janssen, SpringWorks Therapeutics
Matteo Claudio Da Vià
Honoraria: Johnson & Johnson/Janssen, Sanofi
Consulting or Advisory Role: Johnson & Johnson/Janssen, Pfizer, Menarini, Sanofi, Amgen
Speakers' Bureau: Johnson & Johnson/Janssen, Pfizer
Research Funding: Pfizer, Johnson & Johnson/Janssen
Travel, Accommodations, Expenses: Johnson & Johnson/Janssen, Amgen
Claudio de Magistris
Expert Testimony: CSL Behring
Danny DeAvila
Employment: Moffitt
Rafael Renatino Canevarolo
Research Funding: Abbvie
Patents, Royalties, Other Intellectual Property: I have a patent for “A model of clinical synergy in cancer, PCT/US2020/062232 (WO/2021/108551-A1)” pending, a patent for “A multiomic approach to modeling of gene regulatory networks in multiple myeloma, PCT/US2022/024217 (WO/2022/217136-A1)” pending, and a patent for “Altering epigenetic landscapes control progression and refractory disease states in multiple myeloma, PCT/US2023/078667 (WO/2024/097981)” pending
Jamie Teer
Employment: H. Lee Moffitt Cancer Center and Research Institute
Research Funding: Turnstone Bio (Inst)
Patents, Royalties, Other Intellectual Property: Patent application: Large Data Set Negative Information Storage Model
Ariosto S. Silva
Research Funding: Abbvie, Karyopharm Therapeutics
Patents, Royalties, Other Intellectual Property: U.S. Patent Application #63/173,389 SELINEXOR SYNERGISM IN MULTIPLE MYELOMA, U.S. Patent Application #63/301,507 A MULTIOMIC APPROACH TO MODELING OF GENE REGULATORY NETWORKS IN MULTIPLE MYELOMA
Rachid Baz
Honoraria: Hikma Pharmaceuticals
Consulting or Advisory Role: BMS, Sanofi, Janssen, Pfizer
Research Funding: Karyopharm Therapeutics (Inst), Abbvie (Inst), Janssen (Inst), BMS (Inst), Daiichi Sankyo/Lilly (I), Seagen (I), Regeneron (I), Zymeworks (I), Arvinas (I), Marker Therapeutics (I), Pionyr (I), Quantum Leap Healthcare Collaborative (I)
Other Relationship: GlaxoSmithKline
Krina K. Patel
Leadership: Bristol Myers Squibb/Celgene, oricel, Kite, a Gilead company
Consulting or Advisory Role: Celgene, Bristol Myers Squibb, Janssen, Pfizer, Merck, Takeda, Abbvie, AstraZeneca, Legend Biotech, Novartis, Regeneron
Research Funding: Celgene/Bristol Myers Squibb, Janssen, Nektar, Abbvie/Genentech, Indapta Therapeutics
Travel, Accommodations, Expenses: Bristol Myers Squibb
Peter Campbell
Employment: Quotient Therapeutics Ltd
Stock and Other Ownership Interests: Quotient Therapeutics Ltd
Consulting or Advisory Role: Quotient Therapeutics
Gareth Morgan
Honoraria: BMS, Janssen, Genentech, Sanofi, Takeda, GlaxoSmithKline, Regeneron
Consulting or Advisory Role: Takeda, Takeda, GlaxoSmithKline
Travel, Accommodations, Expenses: BMS, Janssen
Rafael Fonseca
Employment: Mayo Clinic, Mayo Clinic
Leadership: Antengene
Stock and Other Ownership Interests: Caris Life Sciences
Honoraria: Celgene, Bristol Myers Squibb, Bayer, Amgen, Janssen, Kite, a Gilead company, Merck Sharp & Dohme, Juno Therapeutics, Takeda, Abbvie, Aduro Biotech, Sanofi, GlaxoSmithKline, Pfizer, Regeneron, RA Capital
Consulting or Advisory Role: Celgene, Bristol Myers Squibb, Bayer, Amgen, Janssen, Abbvie, Kite, a Gilead company, Merck Sharp & Dohme, Juno Therapeutics, Takeda, Aduro Biotech, Sanofi, OncoTracker, GlaxoSmithKline, Karyopharm Therapeutics, Oncopeptides, Pfizer, Pharmacyclics, Regeneron, RA Capital, Adaptive Biotechnologies
Patents, Royalties, Other Intellectual Property: Dr Fonseca has received a patent for the prognostication of MM based on genetic categorization of the disease
Travel, Accommodations, Expenses: Celgene, Bristol Myers Squibb, Bayer, Amgen, Janssen, Kite, a Gilead company, Merck Sharp & Dohme, Juno Therapeutics, Takeda, Abbvie, Aduro Biotech, Sanofi, OncoTracker
Other Relationship: Adaptive Biotechnologies, Caris Life Sciences
Ola Landgren
Honoraria: Celgene, Bristol Myers Squibb, Medscape, Amgen, Janssen, Karyopharm Therapeutics, GlaxoSmithKline
Consulting or Advisory Role: Bristol Myers Squibb, Celgene, Janssen, GlaxoSmithKline, Pfizer
Research Funding: Amgen (Inst), Janssen (Inst), Pfizer (Inst), Menarini Silicon Biosystems (Inst), Sebia (Inst)
Robert Z. Orlowski
Stock and Other Ownership Interests: Asylia Therapeutics, Inc
Consulting or Advisory Role: Bristol Myers Squibb, Janssen, Sanofi, Takeda, Regeneron, GlaxoSmithKline, BioTheryX, Meridian Therapeutics, Monte Rosa Therapeutics, IASO Biotherapeutics, Lytica Therapeutics
Research Funding: Heidelberg Pharma (Inst), Asylia Therapeutics, Inc (Inst), CARsgen Therapeutics (Inst), Sanofi/Aventis (Inst), Exelixis (Inst), Takeda (Inst), Janssen Biotech (Inst), Celgene/Bristol Myers Squibb (Inst)
Patents, Royalties, Other Intellectual Property: Asylia Therapeutics, Inc
Kenneth H. Shain
Honoraria: Bristol Myers Squibb (Celgene), Janssen Oncology, Amgen, GlaxoSmithKline, Sanofi, Abbvie, Karyopharm Therapeutics, Sebia
Research Funding: Pfizer, Abbvie, Karyopharm Therapeutics
Niccolo Bolli
Honoraria: Janssen, GlaxoSmithKline, Pfizer, Jazz Pharmaceuticals, Amgen, Sanofi, Menarini, Oncopeptides
Consulting or Advisory Role: Pfizer, Takeda, Janssen, GlaxoSmithKline
Research Funding: Janssen
Travel, Accommodations, Expenses: Janssen
Saad Usmani
Consulting or Advisory Role: Janssen Oncology, GlaxoSmithKline, Abbvie, Genentech, Bristol Myers Squibb/Celgene, Regeneron, Oricell Therapeutics, AstraZeneca
Research Funding: Janssen Oncology, Bristol Myers Squibb, GlaxoSmithKline, Gracell Therapeutics, Gilead Sciences, K36 Therapeutics
S. Vincent Rajkumar
Honoraria: Research to Practice, Medscape
Patents, Royalties, Other Intellectual Property: Authorship Royalties from Up To Date
No other potential conflicts of interest were reported.
REFERENCES
1. Kyle RA: Monoclonal gammopathy of undetermined significance. Natural history in 241 cases. Am J Med 64:814-826, 1978
2. Kyle RA, Greipp PR: Smoldering multiple myeloma. N Engl J Med 302:1347-1349, 1980
3. Landgren O, Kyle RA, Pfeiffer RM, et al. : Monoclonal gammopathy of undetermined significance (MGUS) consistently precedes multiple myeloma: A prospective study. Blood 113:5412-5417, 2009
4. Maura F, Bolli N, Rustad EH, et al. : Moving from cancer burden to cancer genomics for smoldering myeloma: A review. JAMA Oncol 6:425-432, 2020
5. Rajkumar SV, Landgren O, Mateos MV: Smoldering multiple myeloma. Blood 125:3069-3075, 2015
6. Rajkumar SV, Dimopoulos MA, Palumbo A, et al. : International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol 15:e538-e548, 2014
7. Kyle RA, Larson DR, Therneau TM, et al. : Long-term follow-up of monoclonal gammopathy of undetermined significance. N Engl J Med 378:241-249, 2018
8. Kyle RA, Therneau TM, Rajkumar SV, et al. : Prevalence of monoclonal gammopathy of undetermined significance. N Engl J Med 354:1362-1369, 2006
9. Murray D, Kumar SK, Kyle RA, et al. : Detection and prevalence of monoclonal gammopathy of undetermined significance: A study utilizing mass spectrometry-based monoclonal immunoglobulin rapid accurate mass measurement. Blood Cancer J 9:102, 2019
10. Thorsteinsdottir S, Gislason GK, Aspelund T, et al. : Prevalence of smoldering multiple myeloma based on nationwide screening. Nat Med 29:467-472, 2023
11. International Myeloma Working Group : Criteria for the classification of monoclonal gammopathies, multiple myeloma and related disorders: A report of the International Myeloma Working Group. Br J Haematol 121:749-757, 2003
12. Kazandjian D, Hill E, Dew A, et al. : Carfilzomib, lenalidomide, and dexamethasone followed by lenalidomide maintenance for prevention of symptomatic multiple myeloma in patients with high-risk smoldering myeloma: A phase 2 nonrandomized controlled trial. JAMA Oncol 7:1678-1685, 2021
13. Landgren CO, Chari A, Cohen YC, et al. : Daratumumab monotherapy for patients with intermediate-risk or high-risk smoldering multiple myeloma: A randomized, open-label, multicenter, phase 2 study (CENTAURUS). Leukemia 34:1840-1852, 2020
14. Mateos MV, San Miguel JF: Treatment for high-risk smoldering myeloma. N Engl J Med 369:1764-1765, 2013
15. Sklavenitis-Pistofidis R, Aranha MP, Redd RA, et al. : Immune biomarkers of response to immunotherapy in patients with high-risk smoldering myeloma. Cancer Cell 40:1358-1373.e8, 2022
16. Dimopoulos MA, Voorhees PM, Schjesvold F, et al. : Daratumumab or active monitoring for high-risk smoldering multiple myeloma. N Engl J Med 392:1777-1788, 2025
17. Lonial S, Jacobus S, Fonseca R, et al. : Randomized trial of lenalidomide versus observation in smoldering multiple myeloma. J Clin Oncol 38:1126-1137, 2020
18. Mateos MV, Hernandez MT, Giraldo P, et al. : Lenalidomide plus dexamethasone for high-risk smoldering multiple myeloma. N Engl J Med 369:438-447, 2013
19. Landgren O, Chari A, Cohen YC, et al. : Efficacy and safety of daratumumab in intermediate/high-risk smoldering multiple myeloma: Final analysis of CENTAURUS. Blood 145:1658-1669, 2025
20. Cherry BM, Korde N, Kwok M, et al. : Modeling progression risk for smoldering multiple myeloma: Results from a prospective clinical study. Leuk Lymphoma 54:2215-2218, 2013
21. Kazandjian D, Diamond B, Papadimitriou M, et al. : Genomic profiling to contextualize the results of intervention for smoldering multiple myeloma. Clin Cancer Res 30:4482-4490, 2024
22. Mateos MV, Kumar S, Dimopoulos MA, et al. : International Myeloma Working Group risk stratification model for smoldering multiple myeloma (SMM). Blood Cancer J 10:102, 2020
23. Perez-Persona E, Vidriales MB, Mateo G, et al. : New criteria to identify risk of progression in monoclonal gammopathy of uncertain significance and smoldering multiple myeloma based on multiparameter flow cytometry analysis of bone marrow plasma cells. Blood 110:2586-2592, 2007
24. Cowan A, Ferrari F, Freeman SS, et al. : Personalised progression prediction in patients with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma (PANGEA): A retrospective, multicohort study. Lancet Haematol 10:e203-e212, 2023
25. Bolli N, Maura F, Minvielle S, et al. : Genomic patterns of progression in smoldering multiple myeloma. Nat Commun 9:3363, 2018
26. Boyle EM, Deshpande S, Tytarenko R, et al. : The molecular make up of smoldering myeloma highlights the evolutionary pathways leading to multiple myeloma. Nat Commun 12:293, 2021
27. Bustoros M, Sklavenitis-Pistofidis R, Park J, et al. : Genomic profiling of smoldering multiple myeloma identifies patients at a high risk of disease progression. J Clin Oncol 38:2380-2389, 2020
28. Mikulasova A, Wardell CP, Murison A, et al. : The spectrum of somatic mutations in monoclonal gammopathy of undetermined significance indicates a less complex genomic landscape than that in multiple myeloma. Haematologica 102:1617-1625, 2017
29. Misund K, Keane N, Stein CK, et al. : MYC dysregulation in the progression of multiple myeloma. Leukemia 34:322-326, 2020
30. Oben B, Froyen G, Maclachlan KH, et al. : Whole-genome sequencing reveals progressive versus stable myeloma precursor conditions as two distinct entities. Nat Commun 12:1861, 2021
31. Bustoros M, Anand S, Sklavenitis-Pistofidis R, et al. : Genetic subtypes of smoldering multiple myeloma are associated with distinct pathogenic phenotypes and clinical outcomes. Nat Commun 13:3449, 2022
32. Reference deleted.
33. Reference deleted.
34. Maura F, Rajanna AR, Ziccheddu B, et al. : Genomic classification and individualized prognosis in multiple myeloma. J Clin Oncol 42:1229-1240, 2024
35. Maclachlan KH, Rustad EH, Derkach A, et al. : Copy number signatures predict chromothripsis and clinical outcomes in newly diagnosed multiple myeloma. Nat Commun 12:5172, 2021
36. Rustad EH, Yellapantula VD, Glodzik D, et al. : Revealing the impact of structural variants in multiple myeloma. Blood Cancer Discov 1:258-273, 2020
37. 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 32:1044-1048, 2018
38. Abbas S, Pich O, Devonshire G, et al. : Mutational signature dynamics shaping the evolution of oesophageal adenocarcinoma. Nat Commun 14:4239, 2023
39. Lawson ARJ, Abascal F, Coorens THH, et al. : Extensive heterogeneity in somatic mutation and selection in the human bladder. Science 370:75-82, 2020
40. Reference deleted.
41. Alexandrov LB, Kim J, Haradhvala NJ, et al. : The repertoire of mutational signatures in human cancer. Nature 578:94-101, 2020
42. Machado HE, Mitchell E, Obro NF, et al. : Diverse mutational landscapes in human lymphocytes. Nature 608:724-732, 2022
43. Rustad EH, Nadeu F, Angelopoulos N, et al. : mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies. Commun Biol 4:424, 2021
44. Gehrung M, Crispin-Ortuzar M, Berman AG, et al. : Triage-driven diagnosis of Barrett's esophagus for early detection of esophageal adenocarcinoma using deep learning. Nat Med 27:833-841, 2021
45. Killcoyne S, Gregson E, Wedge DC, et al. : Genomic copy number predicts esophageal cancer years before transformation. Nat Med 26:1726-1732, 2020
46. Landgren O, Hofmann JN, McShane CM, et al. : Association of immune marker changes with progression of monoclonal gammopathy of undetermined significance to multiple myeloma. JAMA Oncol 5:1293-1301, 2019
47. Maura F, Diamond B, Maclachlan KH, et al. : Initial whole-genome sequencing of plasma cell neoplasms in first responders and recovery workers exposed to the World Trade Center attack of September 11, 2001. Clin Cancer Res 27:2111-2118, 2021
48. Maura F, Kaddoura M, Poos AM, et al. : Temporal genomic dynamics shape clinical trajectory in multiple myeloma. Nat Genet 57:2203-2214, 2025
49. Rustad EH, Yellapantula V, Leongamornlert D, et al. : Timing the initiation of multiple myeloma. Nat Commun 11:1917, 2020
50. Diamond B, Yellapantula V, Rustad EH, et al. : Positive selection as the unifying force for clonal evolution in multiple myeloma. Leukemia 35:1511-1515, 2021
51. Maura F, Landgren O, Morgan GJ: Designing evolutionary-based interception strategies to block the transition from precursor phases to multiple myeloma. Clin Cancer Res 27:15-23, 2021
52. Jain N, Wierda WG, O'Brien S: Chronic lymphocytic leukaemia. Lancet 404:694-706, 2024
53. Bachy E, Linton K: Frontline treatment of follicular lymphoma: What will it take to change current practice? Blood 10.1182/blood.2024026019 [epub ahead of print on January 27, 2025]
54. Go RS, Rajkumar SV: How I manage monoclonal gammopathy of undetermined significance. Blood 131:163-173, 2018
55. Visram A, Larson D, Norman A, et al. : Comparison of progression risk of monoclonal gammopathy of undetermined significance by method of detection. Blood 145:325-333, 2025
56. Das R, Strowig T, Verma R, et al. : Microenvironment-dependent growth of preneoplastic and malignant plasma cells in humanized mice. Nat Med 22:1351-1357, 2016
57. Mateos M-V, Martinez Lopez J, Rodríguez-Otero P, et al. : Curative strategy (GEM-CESAR) for high-risk smoldering myeloma (SMM): Carfilzomib, lenalidomide and dexamethasone (KRd) as induction followed by HDT-ASCT, consolidation with Krd and maintenance with Rd. Blood 138:1829, 2021
58. Alberge JB, Dutta AK, Poletti A, et al. : Genomic landscape of multiple myeloma and its precursor conditions. Nat Genet 57:1493-1503, 2025
59. Maura F, Bergsagel PL: Targeting the tumor and the immune system in smoldering multiple myeloma. N Engl J Med 392:1858-1860, 2025
60. Aktas-Samur A, Fulciniti M, Shah P, et al. : Differentiating between MM like smoldering myeloma (SMM) and non-progressor SMM. Blood 144:1018, 2024
Associated Data
Data Availability Statement
A data sharing statement provided by the authors is available with this article at DOI https://doi.org/10.1200/JCO-25-01733 . The previously published WGS and WES data are available on EGA: EGAD00001006363, EGAD00001003309, and EGAS00001004467, and dbGap: phs001323.v3.p1. The MSKCC WGS data are available through the International Cancer Genome Consortium ( https://platform.icgc-argo.org/ ), were partially generated by the Polyethnic 1000 initiative, and are currently being uploaded on EGA. The Mayo Clinic WGS and WES are available under GSE: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1301307 . The University of Milan WGS data are available on GEO under the accession code: PRJNA1305657. The MDACC genomic data are available in EGA under EGAC50000000282. The molecular data generated at the Moffitt Cancer Center used in this research were generated through private funding by Aster Insights ( www.asterinsights.com ) in collaboration with the Oncology Research Information Exchange Network (ORIEN, www.oriencancer.org ). Requests for access to the raw data (FASTQ/BAM) used in this study can be submitted here at https://researchdatarequest.orienavatar.com/ . The analytical workflows used for this study are available in github: https://github.com/bachisiozic/CNV_mmsig and https://github.com/bachisiozic/SMM_functions .