Multiple myeloma (MM) is characterized by the accumulation of malignant plasma cells in the bone marrow and excessive production of a single monoclonal immunoglobulin. MM is the second leading cause of blood cancer worldwide, which accounts for approximately 10% of hematologic malignancies [1]. A recent increase in the incidence of MM has been described in Asian countries such as Japan, Korea, and Taiwan [2,3,4]. MM is more likely to occur in elderly men [4]. Generally, patients with early-stage MM are without symptoms (asymptomatic); however, symptoms develop following disease progression. The most typical clinical manifestations of MM include hypercalcemia, renal failure, anemia, and bone fractures (CRAB symptoms) [5]. Over the past several years, treatment strategies have expanded extensively for MM patients. The development of new drugs has led to significant improvements in prognosis and overall survival (OS) among patients. Key drugs for treatment are immunomodulatory imide drugs (IMiDs) (thalidomide, lenalidomide, and pomalidomide), proteasome inhibitors (bortezomib, carfilzomib, and ixazomib), monoclonal antibodies (daratumumab and elotuzumab), immune-based therapies (CAR-T cell and bispecific T-cell engager [BiTE]), and small-molecule inhibitors targeting FGFR3 and RAS/MAPK signaling pathways (erdafitinib, vemurafenib, and cobimetinib) [6, 7]. However, despite recent advances in therapeutic approaches in MM, it remains incurable. Although most patients generally respond to the standard first-line treatment, many of them would inevitably relapse or become refractory to the disease [6].
Conventional karyotyping and fluorescence in situ hybridization (FISH) are the gold standard techniques used for detecting cytogenetic abnormalities in MM. Conventional karyotypes are prepared from mitotic cells that have been arrested at the metaphase stage. About 30–50% of cytogenetic changes in MM can be detected using karyotyping; however, they are frequently acquired from advanced cases [8]. Conventional karyotyping tends to miss chromosomal aberration in early stages of the disease, due to its low mitotic index and number of malignant plasma cells. The low resolution of conventional karyotyping has also limited the identification of cryptic, subtle, or complex chromosomal changes. FISH, on the other hand, is more sensitive than classical cytogenetics. FISH uses interphase nuclei and thus does not require cell proliferation. It uses fluorescence-labeled probes to detect specific DNA sequences on the chromosome. Consequently, up to 80% of chromosomal aberration in MM can be identified by FISH [9]. The sensitivity of FISH testing can be enhanced with plasma cell enrichment of bone marrow specimens. Nevertheless, the main disadvantage of FISH is that it can only detect known chromosomal aberrations. Recently, with the progress in molecular techniques such as gene expression profiling (GEP) and next-generation sequencing (NGS), various driver gene mutations, oncogenic dependencies, structural variants (SVs), chromoplexy, and chromothripsis in relation to MM clonality and evolution have been discovered. Although GEP and NGS have paved a way toward a more effective management of MM patients, most of the developing countries in Asia, including Malaysia, are still relying on conventional methods (G-banding karyotype and FISH) to detect structural and numerical chromosomal aberrations in MM. The current challenge is whether new emerging molecular techniques should be adopted in the routine clinical practice of MM to improve the diagnosis and management of MM patients.
In this review, we describe the recurrent primary and secondary oncogenic events in MM and their clinical impacts on the prognosis of patients. We also summarize the most prominent findings concerning genomic changes (molecular signatures, new and rare somatic mutations) contributed by GEP and NGS studies of MM. We compiled the relevant articles for this review from electronic databases, including PubMed, Google Scholar, Scopus, and Web of Science. Keywords used for searches included MM, translocation, primary and secondary oncogenic events, chromosomal abnormalities, gene expression, NGS, mutation, clonal evolution, genomic landscape, and SVs. All relevant articles were carefully reviewed, and more articles were searched based on the citation in the articles.
Molecular events in the pathogenesis of MM
MM is a heterogeneous and genetically complex disease. It is thought to occur via multiple primary and secondary oncogenic events (Figure 1) [10]. Hyperdiploidy and translocations involving the IgH gene rearrangement occurring during early B-cell development in germinal centers are primary oncogenic events that initiate the development of MM [11]. The most common primary chromosomal abnormalities in MM are hyperdiploidy, translocations t(4;14)(p16;q32), t(14;16)(q32;q23), t(14;20)(q32;q11), and t(11;14)(q13;q32). Secondary oncogenic events involve copy number aberrations (CNAs) such as monosomy or del(13q), del(1p), gain(1q21), del(17p)/TP53, MYC-associated translocations, and acquired mutations (K-ras, N-ras, BRAF, TP53, etc). Secondary oncogenic events aggravate disease progression and frequently develop at the later stage of the disease [11]. The common primary and secondary chromosomal abnormalities in MM and their affected genes or chromosomes, frequency, risk stratification, and clinical prognosis are summarized in Table 1.
Figure 1. Primary and secondary oncogenic events in the molecular pathogenesis of MM. MGUS, monoclonal gammopathy of undetermined significance; MM, multiple myeloma; SMM, smoldering multiple myeloma.
Table 1.
Common primary and secondary chromosomal abnormalities and their prognostic outcomes in MM
Chromosomal abnormality | Genes/chromosomes affected | Frequency (%) | Risk stratification | Prognosis | Remarks | References |
---|---|---|---|---|---|---|
Primary oncogenic events | ||||||
Hyperdiploidy | Trisomies of odd-numbered chromosomes | 50–60 | Standard risk | Favorable |
Trisomy 21-negative impact on OS |
[12,13,14,15,16,17] |
IgH translocations | 60 | Occur as early as the MGUS stage |
[12, 18, 19] | |||
t(4;14)(p16;q32) | FGFR3/ MMSET | 15 | High risk | Unfavorable/neutral |
Commonly developed from errors in CSR |
[10, 17, 20,21,22,23,24,25] |
t(14;16)(q32;q23) | c-MAF | 5–7 | High risk | Unfavorable | [14, 19, 20, 26] | |
t(14;20)(q32;q11) | MAFB | 1–2 | High risk | Unfavorable | [14, 20, 27] | |
t(11;14)(q13;q32) | CCND1 | 15–20 | Standard risk | Favorable/neutral | Commonly caused by SHM |
[10, 28,29,30] |
Others: | ||||||
t(12;14)(p13;q32) | CCND2 | <1 | Standard risk | Favorable/neutral | [19] | |
t(6;14)(p21;q32) | CCND3 | 2 | Standard risk | Favorable/neutral | ||
t(8;14)(q24.3;q32) | MAFA | <1 | High risk | Unfavorable | ||
Secondary oncogenic events | ||||||
del(1p) | CDKN2C/ FAM46C/ FaF1 | 30 | High risk | Unfavorable |
1p21 and 1p32 are the most frequently deleted regions | [31,32,33] |
Gain (1q21) | CKS1B/ PSMD4 | 40 | High risk | Unfavorable |
Rarely found in MGUS |
[34,35,36,37] |
Monosomy 13/ del(13q) | DIS3/ RB1 | 50 | Neutral | 85% are monosomy; 15% are partial deletion |
[8, 32, 38,39,40] | |
del(17p) | TP53 | 5–10 NDMM |
High risk | Unfavorable |
Late event in pathogenesis |
[11, 41, 42] |
MYC translocation | MYC/ IgK/ IgL/ IgH/ FOXO3/ FAM46C/ TXNDC5/ BMP6 | 15 early-stage MM |
Unfavorable/neutral |
IgH/MYC or IgK/MYC are commonly associated with disease progression |
[11, 12, 42,43,44,45,46,47,48,49] | |
Others | ||||||
del(11q) | BIRC2/3 | 7 | [12, 32, 50, 51] | |||
del(16q) | WWOX/ CYLD | 35 | Unfavorable | |||
del(14q) | TRAF3 | 38 | Unfavorable | |||
del(12p) | CDKN1B | Unfavorable | ||||
del(8p) | TRAIL | Unfavorable |
CSR, class-switch recombination; MGUS, monoclonal gammopathy of undetermined significance; MM, multiple myeloma; NDMM, newly diagnosed multiple myeloma; OS, overall survival; PFS, progression-free survival; RRMM, relapsed or refractory multiple myeloma; SHM, somatic hypermutation; SMM, smoldering multiple myeloma.
Double and triple hits in MM
The co-occurrence of two or three high-risk genetic alterations is defined as double-/triple-hit MM. Based on the criteria stated in the IMWG, clinical outcomes of double-hit MM are similar regardless of whether they are classified as high-risk or low/standard-risk MM. Thus, defining the molecular features of double-hit MM is important to improve risk stratification and outcome prediction in MM. Double-hit MM is found in 1 of 33 newly diagnosed multiple myeloma (NDMM) [40]. According to Baysal et al. [52], the OS of MM patients with double-hit, single high-risk, and no high-risk MM were 6 months, 32 months, and 57 months, respectively. The same study found that the hazard ratio was worsened in triple-hit (7.30) than double-hit (5.55) and single high-risk (1.42) MM patients [52]. Another study revealed a close relationship between the co-occurrence of translocation t(4;14) and TP53 mutation or del(17p) and TP53 mutation, where they were found to confer poor prognosis and OS in the patients [53]. By using genome-wide NGS analysis, two genomic variables in double-hit MM were defined according to the background of International Staging System (ISS) III [54]. They were the biallelic inactivation of TP53 and amplification (≥4 copies) of CKS1B (1q21). Biallelic inactivation of TP53 has an adverse prognosis compared to wild-type or monoallelic inactivation of TP53 [54]. Amplification of CKS1B has a shorter progression-free survival (PFS) and OS than a gain of 1q. There was no significant change in PFS when amplification of 1q co-occurred with biallelic TP53, del(17p), t(4;14), or t(14;16) [54]. Double/triple hits exhibited extra-high-risk features in MM, and the higher is the number of “hits,” the lower is the OS of the patients. In addition to the number of “hits,” the type of double/triple hits also significantly influences the prognosis outcomes and OS of the patients. Thus, patients presented with double/triple hits may require more intensive treatment than those with standard-risk MM, suggesting the importance of defining subtle genetic differences in patients using high-end technology to improve prognostic and treatment outcomes.
GEP in MM
Transcriptome changes play a critical role in myelomagenesis as early as in the monoclonal gammopathy of undetermined significance (MGUS) stage [55]. In the past decades, GEP or microarray has emerged as one of the most popular approaches in the identification of transcriptome changes, molecular mechanisms, and pathways underlying human cancers. GEP has contributed to the characterization of disease subtypes, risk stratification, prognosis, and outcomes in MM patients. One of the most prominent results from GEP was the discovery of a 70-gene signature model by the University of Arkansas for Medical Sciences (UAMS) in 2007 [56]. The 70-gene signature was found to be superior in risk and prognostic stratification of NDMM. They found that most of the crucial genes related to disease progression were localized in chromosome 1. From the same study, 17-gene subsets for high-risk MM were also revealed. Among the important genes found in the 17-gene subset prediction model are CKS1B, ASPM, and CTBS.
Another significant study was reported by Broyl et al. [57]. They discovered seven unique clusters, which were associated with NDMM based on the gene expression profiles: i. translocation [MMSET/t(4;14), MAF/t(14;16)/t(14;20), cyclin D1/t(11;14), and cyclin D2/t(6;14)]; ii. hyperdiploidy (HY); iii. proliferation-associated genes (PR); iv. low percentage of bone disease (LB); v. overexpression of cancer testis antigens (CTAs); vi. NFκB pathway; and vii. overexpression of protein tyrosine phosphatases (PRLs) [57]. Among the seven clusters, three were newly identified (v–vii), while the rest were consistent with the UAMS classification [58].
The EMC-92 gene signature prediction model for high-risk MM was developed by another group of scientists from the Netherlands based on the findings from the GEP [59]. Survivin/BIRC5, FGFR3, and STAT1 are the key genes in the EMC-92 model. Recent combination analysis of EMC-92, UAMS70, ISS, and FISH [t(4;14) & del(17p13)] has shown that EMC-92 gene classifier + ISS was the most superior marker for risk stratification in MM patients [59, 60]. This sheds light on the application of GEP in prognosis and risk prediction, replacing FISH, which is relatively expensive and laborious. FISH, however, can be used as a confirmation test if required.
NGS
NGS of MM
Although GEP has been successfully used to characterize MM into different clinicopathological subtypes and molecular signatures for diagnosis, staging, risk stratification, and prognostication of MM, data generated by GEP are inadequate to fully delineate the molecular biology of this highly genetically complex disease. In recent years, NGS has become a promising tool in the study of IgH translocations, V(D)J clonal rearrangements, IgH isotype, CNAs, and somatic mutations simultaneously in a more refined manner.
The very first mutational profiles generated by NGS data in the past 10 years revealed that there is no specific or unique mutation in MM. The observation remains valid in the latest findings [61, 62]. To date, approximately 250 mutated genes have been found in MM, and about a quarter of them are identified as driver genes [62]. N-Ras, K-Ras, DIS3, FAM46C, TP53, and BRAF are considered the top recurrent or driver genes in MM, with a mutation frequency range between 6%–25% each (Figure 2) [53, 61, 63, 64]. Mutation in other driver genes such as TRAF3, SP140, IRF4, JAK3, and PPGFRA are rare with low recurrence rates (<5%) [65, 66]. Other newly identified potential driver genes are IDH1, IDH2, HUWE1, KLHL6, and PTPN11 [67]. N-Ras, K-Ras, and BRAF are key components of the MAPK pathway [61, 68, 69]. Studies showed that the MEK-ERK signaling pathway was affected by BRAF and RAS mutations in >50% of the cases [70]. The most common site of N-Ras mutation is in codon Q61, while in K-Ras, it is in codons G12, G13, and Q61 [67]. On the other hand, BRAF mutation is predominantly seen in codon D594 (92%) in the t(14;16) group and BRAF V600E in the t(4;14) group [67]. The discovery of mutations in BRAF allowed many MM patients to benefit from BRAF inhibitor (vemurafenib) treatment [10].
Figure 2. Recurrent gene aberrations and their frequency in MM. MM, Multiple myeloma.
NGS findings not only confirm the heterogeneous complexity of MM but also show that most of the driver mutations are present in the subclonal population, and multiple mutations are detected in different genes within the same pathway, suggesting a diverse pattern of clonal evolution in MM, which evolves through space and time [10, 70]. The main clonal evolution in MM includes neutral evolution, branching evolution, and linear evolution [10, 71]. Neutral evolution occurs when all descendant subclones show a similar ability to survive under certain circumstances, and the evolutionary changes are not caused by selective pressure. Branching evolution is the early divergence of subclones with different mutations, which evolves further over time, possibly driven by the selective pressure of the bone marrow microenvironment and treatment, or inherent tumor characteristics, or both. In linear evolution, a single subclonal population is fully substituted by another highly adapted subclone [10, 71]. The main clonal evolution models in MM are visualized in Figure 3. Interestingly, recent NGS studies have shown that the emergence of multiple selective clonal sweeps derived from single cells can drive relapse even after 10 years of remission, suggesting the dormancy of resistant clones [72].
Figure 3. Main clonal evolution models in multiple myeloma. Each color represents a single subclone.
Despite the discovery of driver gene mutations and clonal evolution in MM, another major contribution by NGS was the uncovering of complex SVs in MM. Complex SVs are usually difficult to detect using conventional sequencing approaches. Common complex SVs in MM include chromothripsis, chromoplexy, and multiple templated insertions [73]. Generally, MM patients present with at least one complex SV (80%) [38]. Chromothripsis, a form of chromoanagenesis, was first detected in chronic lymphoblastic leukemia (CLL) [74]. Chromothripsis is characterized by ten to hundreds of chromosomes breaking and rejoining in confined genomic regions in one or a few chromosomes (Figure 4). In contrast, chromoplexy involved a larger number of chromosomal rearrangements than chromothripsis (Figure 4). It was first identified in prostate cancer using whole-genome sequencing (WGS) [75]. Chromothripsis and templated insertions were found to be associated with clonal events, which occur early in the disease course, while chromoplexy is a late event that is possibly related to disease progression, drug resistance, and relapse [38]. There is a strong association between chromothripsis and adverse clinical outcomes in NDMM [76]. Templated insertions are less common than chromothripsis (19% vs 24%) [77]. It is characterized by focal gains bounded by translocations, resulting in the concatenation of amplified segments from two or more chromosomes into a continuous stretch of DNA, which is inserted back into any of the related chromosomes. SV hot spots frequently implicate known MM oncogenes or tumor suppressor genes such as Ig, MYC, CCND1, MMSET, IRF4, MAP3K14, FAM46C, CDKN2C, CYLD, and SP140 [38, 78]. Recently, novel SVs involving critical genes in immune-based therapies TNFRSF17, SLAMF7, and BCMA were also revealed [78]. Patients who harbored SVs involving these genes tend not to respond to elotuzumab, BiTEs, or CAR-T cell therapy, or to their combination. NGS of MM also discovered various SVs hot spots in novel potential driver genes such as IRF2, BTG2, SOX30, NEDD9, KLF13, and TNFRSF13B [78].
Figure 4. Complex chromosomal rearrangements in multiple myeloma: chromothripsis and chromoplexy.
NGS also contributes to the revelation of the activity-induced deaminase (AID)/apolipoprotein B mRNA editing enzyme and catalytic polypeptide-like (APOBEC) mutational signatures in MM. AID or APOBEC plays an important role in mediating somatic mutations and genomic instability in MM. The role of AID in initiating oncogenesis of MM has long been studied. AID is an essential regulator of somatic hypermutation and class-switch recombination of Ig genes. AID initiates C to U mismatches in single-stranded DNA and preferentially deaminates WRC/GYW hot spots (W=A/T, R=A/G, Y=C/T) [79]. AID is also a crucial mutator for IgH/MYC chromosomal translocation and oncogene mutation in MM [80]. So far, many AID-target driver genes have been identified. Among the most significant were EGR1, IRF4, LTB, PIM1, TET2, and XBP1 [81]. Mutations in AID-target driver genes provide a selective growth advantage in the early phase of the disease [53].
On the other hand, APOBEC family proteins consist of 10 enzymes, namely, A1, A2, A3A, A3B, A3C, A3D, A3F, A3G, A3H, and A4. APOBEC is critical in mRNA editing and antiviral defense in the cells [82]. Dysregulation of APOBEC causes C>T, C>G, and C>A mutations in a TpC context and thereby resulting in copy number and mutational changes. In contrast to AID, APOBEC mutational activity is likely correlated with late events in disease progression [53]. High expression of APOBEC promotes DNA damage and increases mutational burden in MM (e.g., APOBEC3G) [82, 83]. Studies have shown that aberrant APOBEC expression is associated with translocations t(14;16)/MAF and t(14;20)/MAFB and a poor prognosis impact in MM [83]. However, only a small fraction of patients who presented with MAF/MAFB/MAFA translocations were related to high APOBEC activity (~23%), suggesting that other factors may be involved in regulating MAF-related translocations [84]. Co-occurrence of APOBEC activity and ISS stage III reduced OS in MM patients when compared to patients without the risk factors (53.8% vs 93.3%) [84]. Interestingly, APOBEC also triggers kataegis, a pattern of localized hypermutations characterized by C>T transitions at TpC dinucleotides. Kataegis has been observed at the MYC/IgK or IgL translocation breakpoints, implicating the co-occurrence of translocations and APOBEC-associated mutations in MM [85]. Suppression of APOBEC in MM cell lines was shown to reduce genomic and mutational changes by 30–65% [82]. A recent study that investigated the genomic spectrum of smoldering multiple myeloma (SMM) has shown that SMM patients with APOBEC-associated mutations demonstrated a shorter time of progression to MM [42]. In other words, the presence of APOBEC mutational signatures could be an indicator for higher risk of progression from SMM to symptomatic MM. Therefore, APOBEC inhibitors are potential therapeutic agents that can be used to suppress genome evolution in SMM or MM as a measure to prevent or delay the progression of the disease. However, this approach is still being developed. Nonetheless, due to the negative impact of APOBEC activity in prognostic and OS of MM patients, it is worth to include APOBEC analysis early on at the diagnosis stage to improve the treatment choice and efficacy in patients with high APOBEC activity.
NGS of MGUS or SMM
The genomic spectrum of MGUS and SMM has been studied using NGS as well, although they are not as much as in MM. MGUS is a benign noncancerous condition, with a ~1% progression rate to MM per year. In contrast, SMM is a precancerous or early-stage MM, with a 10% annual progression risk to active MM in the first 5 years of diagnosis [86].
By comparing NGS data from paired SMM and MM from the same individual, Bolli et al. [53] postulated that most of the driver mutations such as hyperdiploidy, IgH translocations, 1q gain, and del(13q) in SMM were clonal and retained in MM. They found 2 unique clusters during the progression of SMM into MM: the static progression model, in which the same subclonal architecture in SMM was retained in MM; spontaneous evolution cluster, where the additional mutation in subclonal composition was acquired during disease progression [53]. Their findings support the clonal evolution patterns that have been described before [10]. Importantly, their results suggest different management and treatment strategies for the two clusters. Another study was published in 2020, which described the genomic signatures of SMM and its association with risk progression to MM [42]. The group analyzed the genomic profiles of 214 SMM patients using whole-exome sequencing (WES) or deep-targeted sequencing approaches. Their study findings were consistent with Bolli et al. [53], which demonstrated that most of the driver mutations previously defined in MM were already present in SMM patients. These driver mutations were also identified as independent risk factors of progression to MM [42]. In addition, their findings have shown that SMM, which presented with genetic alteration related to the MAPK pathway, DNA repair pathway, or MYC, has a higher risk of progression to MM.
In addition to comparing genomic data between SMM and MM, WGS has also been used to study the differences in the genomic landscape and temporal acquisition of myeloma-associated genomic events between clinically stable and progressive myeloma precursor conditions (MGUS and SMM) [87]. Compared with the clinically progressive myeloma precursor condition, the clinically stable myeloma precursor condition was associated with late initiation of the first clonal copy number alteration in patients’ life and absence or lower number of mutations in driver genes (genes involved in MAPK and NF-κB pathways, TP53), aneuploidy (gain of 1q, deletion of 6q, gain of 8q24, deletion of 8p, and deletion of 16q), templated insertions, chromothripsis, and APOBEC-associated mutational activity.
Conclusions
In summary, advances in technology have expanded our knowledge of tumor heterogeneity, mutational landscape, clonal composition, and dynamic evolution of MM. Evidence has confirmed that myeloma is a highly heterogeneous disease, and one single treatment regimen does not fit all MM patients. Thus, precision medicine will become inevitably important in future myeloma therapy. NGS, which allows the analysis of the full spectrum of recurrent mutations and chromosomal abnormalities in MM, can become a powerful tool toward precision medicine in the treatment of MM. Importantly, NGS also enables rapid and accurate detection of clonal and subclonal mutations, which present at low variant allele frequencies in patients. Many of these subtle genetic changes have a significant influence on the drug efficacy and prognosis outcomes of the patients. NGS has been proven to provide significant clinical benefits and should be transitioned from research to clinical use. However, NGS is laborious, time-consuming, and remains expensive even today, thus preventing its routine use in clinical settings. Malaysia, one of the developing countries in Southeast Asia, also faces the same challenges. So far, we are relying on conventional karyotyping and FISH for routine diagnosis, risk assessment, and therapeutic selection of MM patients. As discussed earlier, karyotyping and FISH have limited resolution and therefore are inadequate to capture the complexity of the MM genome. As technological advances will continue to push NGS toward lower costs with more user-friendly methodologies and data analysis pipelines, we hope that NGS will be integrated into routine clinical practice at diagnosis/relapse to guide disease management and allow for precision medicine in future.
Acknowledgements
The authors would like to thank the Director General of Health and Director of the Institute for Medical Research (IMR), Malaysia for their approval and support to publish this review. We did not receive any specific grant for this research from any funding agency in the public, commercial, or not-for-profit sectors.
Author contributions.
IPNB contributed to the design of the review and data acquisition and wrote the manuscript. IPNB and EE critically reviewed and revised the manuscript and approved the final version submitted for publication and take responsibility for statements made in the published article.
Conflict of interest statement.
The authors have completed and submitted the International Committee of Medical Journal Editors Disclosure Form. Apart from the funding listed in the acknowledgments, none of the authors have any competing relationship, activity, or interest to disclose.
Data sharing statement.
The present review is based on references cited. Further details, opinions, and interpretation are available from the corresponding author on reasonable request.
[1] Bird SA, Boyd K. Multiple myeloma: an overview of management. Palliat Care Soc Pract. 2019; 13. doi: 10.1177/117822421986823
[2] Ozaki S, Handa H, Saitoh T, Murakami H, Itagaki M, Asaoku H, et al. Trends of survival in patients with multiple myeloma in Japan: a multicenter retrospective collaborative study of the Japanese Society of Myeloma. Blood Cancer J. 2015; 5:e349. doi: 10.1038/bcj.2015.79
[3] Lee SJ, Tien HF, Park HJ, Kim JA, Lee DS. Gradual increase of chronic lymphocytic leukemia incidence in Korea, 1999–2010: comparison to plasma cell myeloma. Leuk Lymphoma. 2016; 57:585–9.
[4] Tang CH, Liu HY, Hou HA, Qiu H, Huang KC, Siggins S, et al. Epidemiology of multiple myeloma in Taiwan, a population based study. Cancer Epidemiol. 2018; 55:136–41.
[5] Durie BG, Salmon SE. A clinical staging system for multiple myeloma. Correlation of measured myeloma cell mass with presenting clinical features, response to treatment, and survival. Cancer. 1975; 36:842–54.
[6] Mogollón P, Díaz-Tejedor A, Algarín EM, Paíno T, Garayoa M, Ocio EM. Biological background of resistance to current standards of care in multiple myeloma. Cells. 2019; 8:1432. doi: 10.3390/cells8111432
[7] Pan D, Richter J. Where we stand with precision therapeutics in myeloma: prosperity, promises, and pipedreams. Front Oncol. 2022; 11:819127. doi: 10.3389/fonc.2021.819127
[8] Saxe D, Seo EJ, Bergeron MB, Han JY. Recent advances in cytogenetic characterization of multiple myeloma. Int J Lab Hematol. 2019; 41:5–14.
[9] Hu L, Ru K, Zhang L, Huang Y, Zhu X, Liu H, et al. Fluorescence in situ hybridization (FISH): an increasingly demanded tool for biomarker research and personalized medicine. Biomark Res. 2014; 2:3. doi: 10.1186/2050-7771-2-3
[10] Corre J, Munshi N, Avet-Loiseau H. Genetics of multiple myeloma: another heterogeneity level? Blood. 2015; 125:1870–6.
[11] Barwick BG, Gupta VA, Vertino PM, Boise LH. Cell of origin and genetic alterations in the pathogenesis of multiple myeloma. Front Immunol. 2019; 10:1121. doi: 10.3389/fimmu.2019.01121
[12] Prideaux SM, Conway O’Brien E, Chevassut TJ. The genetic architecture of multiple myeloma. Adv Hematol. 2014; 2014:864058. doi: 10.1155/2014/864058
[13] Smadja NV, Bastard C, Brigaudeau C, Leroux D, Fruchart C; Groupe Français de Cytogénétique Hématologique. Hypodiploidy is a major prognostic factor in multiple myeloma. Blood. 2001; 98:2229–38.
[14] Fonseca R, Debes-Marun CS, Picken EB, Dewald GW, Bryant SC, Winkler JM, et al. The recurrent IgH translocations are highly associated with nonhyperdiploid variant multiple myeloma. Blood. 2003; 102:2562–7.
[15] Carrasco DR, Tonon G, Huang Y, Zhang Y, Sinha R, Feng B, et al. High-resolution genomic profiles define distinct clinicopathogenetic subgroups of multiple myeloma patients. Cancer Cell. 2006; 9:313–25.
[16] Chng WJ, Santana-Dávila R, Van Wier SA, Ahmann GJ, Jalal SM, Bergsagel PL, et al. Prognostic factors for hyperdiploid-myeloma: effects of chromosome 13 deletions and IgH translocations. Leukemia. 2006; 20:807–13.
[17] Chretien ML, Corre J, Lauwers-Cances V, Magrangeas F, Cleynen A, Yon E, et al. Understanding the role of hyperdiploidy in myeloma prognosis: which trisomies really matter? Blood. 2015; 126:2713–9.
[18] Fonseca R, Barlogie B, Bataille R, Bastard C, Bergsagel PL, Chesi M, et al. Genetics and cytogenetics of multiple myeloma: a workshop report. Cancer Res. 2004; 64:1546–58.
[19] Chng WJ, Glebov O, Bergsagel PL, Kuehl WM. Genetic events in the pathogenesis of multiple myeloma. Best Pract Res Clin Haematol. 2007; 20:571–96.
[20] Tian E, Sawyer JR, Heuck CJ, Zhang Q, van Rhee F, Barlogie B, Epstein J. In multiple myeloma, 14q32 translocations are nonrandom chromosomal fusions driving high expression levels of the respective partner genes. Genes Chromosomes Cancer. 2014; 53:549–57.
[21] Mirabella F, Wu P, Wardell CP, Kaiser MF, Walker BA, Johnson DC, Morgan GJ. MMSET is the key molecular target in t(4;14) myeloma. Blood Cancer J. 2013; 3:e114. doi: 10.1038/bcj.2013.9
[22] Kalff A, Spencer A. The t(4;14) translocation and FGFR3 overexpression in multiple myeloma: prognostic implications and current clinical strategies. Blood Cancer J. 2012; 2:e89. doi: 10.1038/bcj.2012.37
[23] Moreau P, Facon T, Leleu X, Morineau N, Huyghe P, Harousseau JL, et al. Recurrent 14q32 translocations determine the prognosis of multiple myeloma, especially in patients receiving intensive chemotherapy. Blood. 2002; 100:1579–83.
[24] Sibley K, Fenton JA, Dring AM, Ashcroft AJ, Rawstron AC, Morgan GJ. A molecular study of the t(4;14) in multiple myeloma. Br J Haematol. 2002; 118:514–20.
[25] Chan H, Phillips M, Maganti M, Farooki S, Piza Rodriguez G, Masih-Khan E, et al. Single-center experience in treating patients with t(4;14) multiple myeloma with and without planned frontline autologous stem cell transplantation. Clin Lymphoma Myeloma Leuk. 2018; 18:225–34.
[26] Avet-Loiseau H, Malard F, Campion L, Magrangeas F, Sebban C, Lioure B, et al. Translocation t(14;16) and multiple myeloma: is it really an independent prognostic factor? Blood. 2011; 117:2009–11.
[27] Attal M, Harousseau JL, Leyvraz S, Doyen C, Hulin C, Benboubker L, et al. Maintenance therapy with thalidomide improves survival in patients with multiple myeloma. Blood. 2006; 108:3289–94.
[28] Fonseca R, Bailey RJ, Ahmann GJ, Rajkumar SV, Hoyer JD, Lust JA, et al. Genomic abnormalities in monoclonal gammopathy of undetermined significance. Blood. 2002; 100:1417–24.
[29] Chang H, Qi XY, Stewart AK. t(11;14) does not predict long-term survival in myeloma. Leukemia. 2005; 19:1078–9.
[30] Matulis SM, Gupta VA, Neri P, Bahlis NJ, Maciag P, Leverson JD, et al. Functional profiling of venetoclax sensitivity can predict clinical response in multiple myeloma. Leukemia. 2019; 33:1291–6.
[31] Cardona-Benavides IJ, de Ramón C, Gutiérrez NC. Genetic abnormalities in multiple myeloma: prognostic and therapeutic implications. Cells. 2021; 10:336. doi: 10.3390/cells10020336
[32] Walker BA, Leone PE, Chiecchio L, Dickens NJ, Jenner MW, Boyd KD, et al. A compendium of myeloma-associated chromosomal copy number abnormalities and their prognostic value. Blood. 2010; 116:e56–65.
[33] Varma A, Sui D, Milton DR, Tang G, Saini N, Hasan O, et al. Outcome of multiple myeloma with chromosome 1q gain and 1p deletion after autologous hematopoietic stem cell transplantation: propensity score matched analysis. Biol Blood Marrow Transplant. 2020; 26:665–71.
[34] Sonneveld P. Gain of 1q21 in multiple myeloma: from bad to worse? Blood. 2006; 108:1426–7.
[35] Rajan AM, Rajkumar SV. Interpretation of cytogenetic results in multiple myeloma for clinical practice. Blood Cancer J. 2015; 5:e365. doi: 10.1038/bcj.2015.92
[36] An G, Xu Y, Shi L, Shizhen Z, Deng S, Xie Z, et al. Chromosome 1q21 gains confer inferior outcomes in multiple myeloma treated with bortezomib but copy number variation and percentage of plasma cells involved have no additional prognostic value. Haematologica. 2014; 99:353–9.
[37] Grzasko N, Hajek R, Hus M, Chocholska S, Morawska M, Giannopoulos K, et al. Chromosome 1 amplification has similar prognostic value to del(17p13) and t(4;14)(p16;q32) in multiple myeloma patients: analysis of real-life data from the Polish Myeloma Study Group. Leuk Lymphoma. 2017; 58:1–15.
[38] Maura F, Bolli N, Angelopoulos N, Dawson KJ, Leongamornlert D, Martincorena I, et al. Genomic landscape and chronological reconstruction of driver events in multiple myeloma. Nat Commun. 2019; 10:3835. doi: 10.1038/s41467-019-11680-1
[39] Walker BA. The chromosome 13 conundrum in multiple myeloma. Blood Cancer Discov. 2020; 1:16–7.
[40] Binder M, Rajkumar SV, Ketterling RP, Greipp PT, Dispenzieri A, Lacy MQ, et al. Prognostic implications of abnormalities of chromosome 13 and the presence of multiple cytogenetic high-risk abnormalities in newly diagnosed multiple myeloma. Blood Cancer J. 2017; 7:e600. doi: 10.1038/bcj.2017.83
[41] Gozzetti A, Frasconi A, Crupi R. Molecular cytogenetics of multiple myeloma. Austin J Cancer Clin Res. 2014; 1:1020.
[42] Bustoros M, Sklavenitis-Pistofidis R, Park J, Redd R, Zhitomirsky B, Dunford AJ, et al. Genomic profiling of smoldering multiple myeloma identifies patients at a high risk of disease progression. J Clin Oncol. 2020; 38:2380–9.
[43] Jenner MW, Leone PE, Walker BA, Ross FM, Johnson DC, Gonzalez D, et al. Gene mapping and expression analysis of 16q loss of heterozygosity identifies WWOX and CYLD as being important in determining clinical outcome in multiple myeloma. Blood. 2007; 110:3291–300.
[44] Kim GY, Gabrea A, Demchenko YN, Bergsagel L, Roschke AV, Kuehl WM. Complex IGH rearrangements in multiple myeloma: frequent detection discrepancies among three different probe sets. Genes Chromosomes Cancer. 2014; 53:467–74.
[45] Mikulasova A, Ashby C, Tytarenko RG, Qu P, Rosenthal A, Dent JA, et al. Microhomology-mediated end joining drives complex rearrangements and overexpression of MYC and PVT1 in multiple myeloma. Haematologica. 2020; 105:1055–66.
[46] Moreau P, Chanan-Khan A, Roberts AW, Agarwal AB, Facon T, Kumar S, et al. Promising efficacy and acceptable safety of venetoclax plus bortezomib and dexamethasone in relapsed/refractory MM. Blood. 2017; 130:2392–400.
[47] Tomas P, Miroslava V, Jiri M, Jana B, Jaroslav B, Marie J, Vlastimil S. Translocation t(8;14) in multiple myeloma defines patients with very poor prognosis–Single centre experience. Clin Lymphoma Myeloma Leuk. 2015; 15(Suppl 3):e122. doi: 10.1016/j.clml.2015.07.311
[48] Barwick BG, Neri P, Bahlis NJ, Nooka AK, Dhodapkar MV, Jaye DL, et al. Multiple myeloma immunoglobulin lambda translocations portend poor prognosis. Nat Commun. 2019; 10:1911. doi: 10.1038/s41467-019-09555-6
[49] Misund K, Keane N, Stein CK, Asmann YW, Day G, Welsh S, et al. MYC dysregulation in the progression of multiple myeloma. Leukemia. 2020; 34:322–6.
[50] Gmidène A, Saad A, Avet-Loiseau H. 8p21.3 deletion suggesting a probable role of TRAIL-R1 and TRAIL-R2 as candidate tumor suppressor genes in the pathogenesis of multiple myeloma. Med Oncol. 2013; 30:489. doi: 10.1007/s12032-013-0489-8
[51] Gazitt Y. TRAIL is a potent inducer of apoptosis in myeloma cells derived from multiple myeloma patients and is not cytotoxic to hematopoietic stem cells. Leukemia. 1999; 13:1817–24.
[52] Baysal M, Demirci U, Umit E, Kirkizlar HO, Atli EI, Gurkan H, et al. Concepts of double hit and triple hit disease in multiple myeloma, entity and prognostic significance. Sci Rep. 2020; 10:5991. doi: 10.1038/s41598-020-62885-0
[53] Bolli N, Biancon G, Moarii M, Gimondi S, Li Y, de Philippis C, et al. Analysis of the genomic landscape of multiple myeloma highlights novel prognostic markers and disease subgroups. Leukemia. 2018; 32:2604–16.
[54] Walker BA, Mavrommatis K, Wardell CP, Ashby TC, Bauer M, Davies F, et al. A high-risk, double-hit, group of newly diagnosed myeloma identified by genomic analysis. Leukemia. 2019; 33:159–70.
[55] Zhan F, Hardin J, Kordsmeier B, Bumm K, Zheng M, Tian E, et al. Global gene expression profiling of multiple myeloma, monoclonal gammopathy of undetermined significance, and normal bone marrow plasma cells. Blood. 2002; 99:1745–57.
[56] Shaughnessy JD Jr, Zhan F, Burington BE, Huang Y, Colla S, Hanamura I, et al. A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1. Blood. 2007; 109:2276–84.
[57] Broyl A, Hose D, Lokhorst H, de Knegt Y, Peeters J, Jauch A, et al. Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients. Blood. 2010; 116:2543–53.
[58] Zhan F, Huang Y, Colla S, Stewart JP, Hanamura I, Gupta S, et al. The molecular classification of multiple myeloma. Blood. 2006; 108:2020–8.
[59] Kuiper R, Broyl A, de Knegt Y, van Vliet MH, van Beers EH, van der Holt B, et al. A gene expression signature for high-risk multiple myeloma. Leukemia. 2012; 26:2406–13.
[60] Kuiper R, van Duin M, van Vliet MH, Broijl A, van der Holt B, El Jarari L, et al. Prediction of high- and low-risk multiple myeloma based on gene expression and the International Staging System. Blood. 2015; 126:1996–2004.
[61] Chapman MA, Lawrence MS, Keats JJ, Cibulskis K, Sougnez C, Schinzel AC, et al. Initial genome sequencing and analysis of multiple myeloma. Nature. 2011; 471:467–72.
[62] Corre J, Cleynen A, Robiou du Pont S, Buisson L, Bolli N, Attal M, et al. Multiple myeloma clonal evolution in homogeneously treated patients. Leukemia. 2018; 32:2636–47.
[63] 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; 38:675–80.
[64] Hu Y, Chen W, Wang J. Progress in the identification of gene mutations involved in multiple myeloma. Onco Targets Ther. 2019; 12:4075–80.
[65] Walker BA, Boyle EM, Wardell CP, Murison A, Begum DB, Dahir NM, et al. Mutational spectrum, copy number changes, and outcome: results of a sequencing study of patients with newly diagnosed myeloma. J Clin Oncol. 2015; 33:3911–20.
[66] Mulligan G, Lichter DI, Di Bacco A, Blakemore SJ, Berger A, Koenig E, et al. Mutation of NRAS but not KRAS significantly reduces myeloma sensitivity to single-agent bortezomib therapy. Blood. 2014; 123:632–9.
[67] Walker BA, Mavrommatis K, Wardell CP, Ashby TC, Bauer M, Davies FE, et al. Identification of novel mutational drivers reveals oncogene dependencies in multiple myeloma. Blood. 2018; 132:587–97.
[68] Bolli N, Avet-Loiseau H, Wedge DC, Van Loo P, Alexandrov LB, Martincorena I, et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat Commun. 2014; 5:2997. doi: 10.1038/ncomms3997
[69] Lohr JG, Stojanov P, Carter SL, Cruz-Gordillo P, Lawrence MS, Auclair D, et al. Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy. Cancer Cell. 2014; 25:91–101.
[70] Lionetti M, Barbieri M, Todoerti K, Agnelli L, Marzorati S, Fabris S, et al. Molecular spectrum of BRAF, NRAS and KRAS gene mutations in plasma cell dyscrasias: implication for MEK-ERK pathway activation. Oncotarget. 2015; 6:24205–17.
[71] Davis A, Gao R, Navin N. Tumor evolution: linear, branching, neutral or punctuated? Biochim Biophys Acta Rev Cancer. 2017; 1867:151–61.
[72] Rasche L, Schinke C, Maura F, Bauer MA, Ashby C, Deshpande S, et al. The spatio-temporal evolution of multiple myeloma from baseline to relapse-refractory states. Nat Commun. 2022; 13:4517. doi: 10.1038/s41467-022-32145-y
[73] Bergsagel PL, Kuehl WM. Promiscuous structural variants drive myeloma initiation and progression. Blood Cancer Discov. 2020; 1:221–3.
[74] Stephens PJ, Greenman CD, Fu B, Yang F, Bignell GR, Mudie LJ, et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell. 2011; 144:27–40.
[75] Shen MM. Chromoplexy: a new category of complex rearrangements in the cancer genome. Cancer Cell. 2013; 23:567–9.
[76] Ashby C, Boyle EM, Bauer MA, Mikulasova A, Wardell CP, Williams L, et al. Structural variants shape the genomic landscape and clinical outcome of multiple myeloma. Blood Cancer J. 2022; 12:85. doi: 10.1038/s41408-022-00673-x
[77] Wiedmeier-Nutor JE, Bergsagel PL. Review of multiple myeloma genetics including effects on prognosis, response to treatment, and diagnostic workup. Life (Basel). 2022; 12:812. doi: 10.3390/life12060812
[78] Rustad EH, Yellapantula VD, Glodzik D, Maclachlan KH, Diamond B, Boyle EM, et al. Revealing the impact of structural variants in multiple myeloma. Blood Cancer Discov. 2020; 1:258–73.
[79] Tang C, Bagnara D, Chiorazzi N, Scharff MD, MacCarthy T. AID overlapping and polη hotspots are key features of evolutionary variation within the human antibody heavy chain (IGHV) genes. Front Immunol. 2020; 11:788. doi: 10.3389/fimmu.2020.00788
[80] Kotani A, Kakazu N, Tsuruyama T, Okazaki IM, Muramatsu M, Kinoshita K, et al. Activation-induced cytidine deaminase (AID) promotes B cell lymphomagenesis in Emu-cmyc transgenic mice. Proc Natl Acad Sci U S A. 2007; 104:1616–20.
[81] Maura F, Rustad EH, Yellapantula V, Łuksza M, Hoyos D, Maclachlan KH, et al. Role of AID in the temporal pattern of acquisition of driver mutations in multiple myeloma. Leukemia. 2020; 34:1476–80.
[82] Talluri S, Samur MK, Buon L, Kumar S, Potluri LB, Shi J, et al. Dysregulated APOBEC3G causes DNA damage and promotes genomic instability in multiple myeloma. Blood Cancer J. 2021; 11:166. doi: 10.1038/s41408-021-00554-9
[83] Walker BA, Wardell CP, Murison A, Boyle EM, Begum DB, Dahir NM, et al. APOBEC family mutational signatures are associated with poor prognosis translocations in multiple myeloma. Nat Commun. 2015; 6:6997. doi: 10.1038/ncomms7997
[84] Maura F, Petljak M, Lionetti M, Cifola I, Liang W, Pinatel E, et al. Biological and prognostic impact of APOBEC-induced mutations in the spectrum of plasma cell dyscrasias and multiple myeloma cell lines. Leukemia. 2018; 32:1044–8.
[85] Yamazaki H, Shirakawa K, Matsumoto T, Hirabayashi S, Murakawa Y, Kobayashi M, et al. Endogenous APOBEC3B overexpression constitutively generates DNA substitutions and deletions in myeloma cells. Sci Rep. 2019; 9:7122. doi: 10.1038/s41598-019-43575-y
[86] Mateos MV, Kumar S, Dimopoulos MA, González-Calle V, Kastritis E, Hajek R, et al. International Myeloma Working Group risk stratification model for smoldering multiple myeloma (SMM). Blood Cancer J. 2020; 10:102. doi: 10.1038/s41408-020-00366-3
[87] Oben B, Froyen G, Maclachlan KH, Leongamornlert D, Abascal F, Zheng-Lin B, et al. Whole-genome sequencing reveals progressive versus stable myeloma precursor conditions as two distinct entities. Nat Commun. 2021; 12:1861. doi: 10.1038/s41467-021-22140-0
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
© 2023. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Abstract
Multiple myeloma (MM) is the second most common form of blood cancer characterized by clonal expansion of malignant plasma cells within the bone marrow. MM is a complex, progressive, and highly heterogeneous malignancy, which occurs via a multistep transformation process involving primary and secondary oncogenic events. Recent advances in molecular techniques have further expanded our understanding of the mutational landscape, clonal composition, and dynamic evolution patterns of MM. The first part of this review describes the key oncogenic events involved in the initiation and progression of MM, together with their prognostic impact. The latter part highlights the most prominent findings concerning genomic aberrations promoted by gene expression profiling (GEP) and next-generation sequencing (NGS) in MM. This review provides a concise understanding of the molecular pathogenesis of the MM genome and the importance of adopting emerging molecular technology in future clinical management of MM.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
1 Hematology Unit, Cancer Research Center, Institute for Medical Research, National Institute of Health, Ministry of Health, Malaysia