Genetic architecture of multiple myeloma: From somatic alterations to germline susceptibility and clinical implications.

Introduction: multiple myeloma definition

Multiple myeloma (MM) is a clonal plasma cell malignancy characterized by overproduction of monoclonal immunoglobulins (M-proteins) [ 1 ]. This expansion is accompanied by suppression of normal immunoglobulin synthesis and progressive marrow infiltration, which underlies disease pathophysiology.

MM represents the second most common hematological malignancy and accounts for 15% of newly diagnosed hematologic cancers and nearly 20% of related mortality. Incidence in Western populations is 5–7 cases per 100,000 persons per year, with a median diagnostic age near 70 years, underscoring its strong age association [ 2 ]. Incidence rises steeply with age [ 3 ].

Clinically, MM is defined by bone marrow infiltration and end-organ damage. Key features include clonal plasma cell proliferation, monoclonal protein production, and organ dysfunction, most frequently involving bone, hematopoiesis, and renal function. Typical manifestations include osteolytic lesions, anemia, hypercalcemia, and renal dysfunction. Diagnostic criteria require ≥10% clonal plasma cells in the bone marrow or biopsy-proven plasmacytoma plus monoclonal protein and myeloma-defining events [ 4 ].

The etiological basis of MM remains incompletely defined, but accumulating evidence indicates a substantial genetic component. Epidemiological studies have demonstrated a 2- to 4-fold increased risk among first-degree relatives of MM patients [ 5 ], supporting inherited susceptibility and motivating investigation into somatic and germline genetic determinants.

Over the past two decades, therapeutic advances have transformed MM management. Proteasome inhibitors (PIs) and immunomodulatory drugs (IMiDs) have improved overall survival (OS) across successive therapeutic eras [ 6 ]. Current treatment paradigms favor combination regimens integrating PIs and IMiDs for newly diagnosed disease. MM is therefore increasingly managed as a chronic, although still incurable, malignancy. Despite this progress, MM remains largely incurable, mainly due to intrinsic and acquired drug resistance. Clinical and molecular studies show that relapse in nearly universal. Therapeutic refractoriness drives progression and mortality [ 7 ].

At the biological level, resistance arises from multiple mechanisms, including genomic instability, epigenetic alterations, defective apoptotic signaling, immune system dysregulation, and protective interactions between malignant plasma cells and the bone marrow microenvironment [ 8 ]. High-throughput molecular profiling has improved the characterization of resistance mechanisms and strengthened risk stratification, supporting biologically informed treatment selection and the development of mechanism-based therapeutic strategies [ 9 ].

Altogether, these advances support biomarker-driven, risk-adapted therapeutic strategies [ 10 ].

While previous reviews have typically addressed germline susceptibility and somatic evolution in MM as separate domains, or have focused on static descriptions of genomic alterations, this work advances an integrated evolutionary genomics framework that conceptualizes disease initiation and progression as a continuous, multilayered process shaped by inherited genetic background, early clonal selection, and therapy-driven dynamics. By moving beyond a binary germline-versus-somatic perspective, this apporach captures the temporal and biological continuity underlying progression from precursor states to symptomatic disease. Importantly, it provides a clinically oriented scaffold for integrating emerging approaches, such as polygenic risk scores and subclonal reconstruction, into unified models for risk stratification, longitudinal disease monitoring, and treatment adaptation. In this way, the proposed framework not only synthesizes existing knowledge but also offers a conceptual basis for translating genomic complexity into actionable precision oncology strategies.

In this review, we synthesize current evidence supporting this model and discuss its implications for precision oncology in MM.

Risk factors and genetic predisposition

Building on the biological and clinical approach outlined above, MM is recognized as a multifactorial disease arising from the combined effects of aging, environmental exposures, precursor disease states, and inherited genetic susceptibility. These factors interact to shape disease initiation, clonal evolution, and clinical heterogeneity. Epidemiological and molecular studies have identified several determinants that modulate individual risk of disease initiation, progression, and clinical presentation. Together, these determinants inform risk stratification and screening strategies.

Environmental and clinical risk factors

Demographic and population-level determinants

Age represents the strongest independent risk factor for MM. Incidence rises sharply after age 50, peaking around 65, consistent with cumulative mutational burden and immune aging. Forecast models project a marked rise in incident cases over the coming decades. Age is therefore a fundamental component of risk stratification, particularly for screening and early intervention in precursor conditions. Notably, although age is strongly associated with disease incidence, evidence regarding its relationship with specific genomic subtypes remains conflicting. Some studies have reported a higher frequency of IgH translocations, particularly t(4;14), in older patients [ 11 ], whereas others have observed lower rates of these high-risk events in elderly-onset disease, together with distinct mutational profiles and fewer MYC rearrangements [ 12 ]. These discrepancies likely reflect differences in cohort composition, sample size, and analytical approaches, and suggest that age-related differences in genomic architecture and evolutionary trajectories remain incompletely defined.

Consistent sex-related differences in MM incidence have been reported across population registries, with a modest but reproducible male predominance observed worldwide [ 13 ]. Male-to-female incidence ratios range between 1.2:1 and 1.5:1 across diverse geographic regions [ 14 ]. Large population-based datasets confirm that approximately 55–57% of cases occur in men [ 15 ].

MM exhibits significant racial disparities, with African Americans experiencing approximately twice the incidence observed in white populations [ 16 ]. Greenberg et al. proposed that this disparity reflects a higher prevalence of monoclonal gammopathy of undetermined significance (MGUS) rather than increased malignant progression [ 17 ]. This interpretation is supported by more recent large-scale analyses, which show no evidence of differences in progression rates from MGUS to MM across racial groups, indicating that disparities are primarily driven by differences in disease initiation [ 18 ]. These findings reinforce a model in which population-level differences in MGUS prevalence, rather than accelerated progression, account for the higher incidence observed in individuals of African ancestry. Population-level differences have also been described in Chinese cohorts, where patients are diagnosed at a younger median age (∼59 years) and often present with higher disease burden [ 19 ]. Genomic analyses suggest partially distinct mutational landscapes compared with Western cohorts, including differences in NF-κB pathway alterations [ 19 ]. The biological basis of these demographic disparities remains incompletely defined.

Age also modifies prognosis. Increasing age is associated with inferior progression-free survival (PFS) and OS [ 20 ]. Age-stratified genomic analyses indicate shifts in lesion frequency, including distribution of high-risk IgH translocations and gain(1q) across age groups [ 21 ], supporting partially distinct evolutionary trajectories in elderly-onset disease.

Taken together, age, sex, and ancestry define a foundational demographic risk framework in MM. These variables inform population-level risk estimation and screening strategies.

Precursor conditions

Virtually all cases of MM are preceded by asymptomatic precursor conditions, primarily MGUS or smoldering MM (SMM). Epidemiological evidence indicates that MGUS affects approximately 3.2% of individuals older than 50 years, with an average annual progression rate to MM of about 1% [ 22 ]. Progression risk is strongly influenced by monoclonal protein burden, bone marrow infiltration, and genetic background [ 23 ].

SMM is associated with a substantially elevated risk of progression to overt MM, particularly during the first five years following diagnosis [ 24 ]. Solitary plasmacytoma also carries a significant risk of progression to systemic disease. These precursor states define biologically and clinically identifiable high-risk populations.

Environmental, occupational, lifestyle and clinical modifiers

Environmental and occupational exposures are associated with MM susceptibility in multiple epidemiological studies. Meta-analyses report elevated risk in selected occupational groups, including firefighters, who exhibit nearly a 50% increased risk, and hairdressers, with approximately a 40% higher risk [ 25 ]. Farming populations likewise show elevated incidence, particularly in contexts involving pesticide exposure [ 26 ].

At the exposure level, pesticides, herbicides, organic solvents, and petroleum-derived products have been associated with increased MM risk [ 27 ]. Proposed mechanisms include genotoxic stress, chronic inflammation, and DNA damage.

Lifestyle-related factors

Lifestyle factors influence MM risk largely through effects on precursor plasma cell disorders, with obesity emerging as the most consistent modifiable contributor. Obesity has been strongly associated with increased MGUS and MM risk, with reported odds ratios in the range of approximately 1.7–2.0 [ 28 , 29 ]. This association likely reflects chronic inflammatory and metabolic dysregulation.

Large prospective cohort analyses demonstrate a clear dose-dependent association between body mass index (BMI) and MM incidence, with a 17% increase in risk per 5 kg/m² increment in cumulative average adult BMI and a 28% increase per 5 kg/m² rise in young adult BMI [ 29 ]. Obesity has also been associated with inferior clinical outcomes [ 30 ]. Proposed mechanisms include IGF-1 pathway activation, adipokine imbalance, inflammatory cytokine signaling, and adipocyte-stromal interactions. In contrast, tobacco smoking shows no consistent association with MM incidence in large meta-analyses [ 31 ]. However, heavy exposure has been associated with a 2.26-fold increased likelihood of MGUS [ 28 ].

Physical activity appears to exert a modest protective effect, although associations attenuate after adjustment for BMI [ 32 , 33 ].

Dietary patterns may further modulate susceptibility, with plant-based diets associated with lower MM risk [ 34 ]. Vitamin D deficiency has been linked to poorer clinical outcomes rather than disease initiation [ 35 ].

Clinical and immune-related comorbidities as modifying risk factors

Clinical comorbidities and immune-mediated conditions have been investigated as MM risk modifiers, with generally modest and heterogeneous associations. More recent meta-analyses suggest an increased risk, particularly for chronic HCV infection [ 36 , 37 ]. Clinical data further support a biologically relevant interaction between chronic viral infection and plasma cell dyscrasia, including potential modulation of disease progression following antiviral therapy [ 38 ]. Type 2 diabetes mellitus (T2D) shows inconsistent associations [ 39 ].

Emerging evidence implicates gut microbiota composition as a biological modifier of MM susceptibility and progression. Mendelian randomization analyses have identified taxa-specific causal associations, and mechanistic studies suggest that dysbiosis may promote disease progression through immune modulation, nitrogen-recycling pathways, and metabolic crosstalk within the bone marrow microenvironment [ 40 ]. Microbiota composition has been associated with treatment response and minimal residual disease (MRD) status [ 38 ].

Autoimmune diseases have been associated with increased susceptibility to MGUS and MM [ 41 ], supporting a role for chronic immune activation. However, large epidemiological analyses show heterogeneous and generally weak associations. Null or weak associations have been reported for primary Sjögren syndrome [ 42 ], ankylosing spondylitis [ 43 ], and rheumatoid arthritis [ 44 ], while findings in systemic lupus erythematosus are inconsistent. Pernicious anemia and ankylosing spondylitis show the most consistent associations with increased MM risk [ [41] , [43] ]. Interestingly, periodontitis, a chronic oral inflammatory disease, has also been linked to MM risk [ 45 ], suggesting a potential role for systemic inflammatory burden in oncogenesis ( Table 1 ). Table 1 Established and proposed risk factors and susceptibility modifiers in MM. Table 1 dummy alt text Risk factor Association with MM Quantitative estimate Disease stage affected Proposed mechanisms References Advanced age Strong increase in incidence with age Incidence rises exponentially after age 50; peak after 65 MM Accumulated somatic mutations, immune senescence, chronic inflammation [ 46 ] Male sex Higher incidence vs females Male:female ratio ∼1.2–1.5 MM Hormonal and immune modulation; cytogenetic subtype differences [ [13] , [47] ] African / African-American or Asian ancestry Higher incidence vs White populations Different mutational landscape and ∼2-fold higher incidence MGUS/MM Higher MGUS prevalence; genetic susceptibility contribution [ [16] , [17] , [19] , [48] , [49] , [50] ] MGUS Obligatory precursor condition Prevalence ∼3.2% (>50 yrs); progression ∼1%/year MGUS→MM Pre-malignant clonal plasma cell expansion [ [22] , [23] ] SMM High progression risk Highest risk within first 5 years SMM→MM Increased clonal burden and genomic instability [ 51 ] Family history (first-degree relatives) Increased risk ∼2–4-fold increased risk MGUS/MM Germline susceptibility variants; shared familial factors [ 52 , 53 ] Obesity / high BMI Increased MGUS and MM risk OR ∼1.7–2.0; +17% risk per 5 kg/m² adult BMI MGUS/MM Chronic inflammation, IGF-1 signaling, adipokine dysregulation [ 28 , 29 ] Occupational exposure Elevated risk Firefighters ∼+50%; hairdressers ∼+40% MM Chemical and combustion-related exposures [ 25 ] Pesticides / solvents / petroleum products Increased risk Association reported (no pooled estimate) MM Genotoxic stress, inflammatory signaling [ 27 ] Smoking No clear MM association Ever smoking RR/OR ∼0.92 MM No consistent effect observed [ 31 ] Heavy smoking Increased MGUS risk ∼2.26-fold increased likelihood MGUS Genotoxic and oxidative stress [ 28 ] T2D (clinical and genetic evidence) Modest/inconsistent epidemiological associations; genetic variants associated with MM risk Clinical OR ∼1.1–1.6 (observational); genetic variant OR up to ∼2.13 MM/MGUS Metabolic/inflammatory dysregulation; shared genetic susceptibility (Ríos et al., 2015; [ 39 ]) Chronic viral infections (HCV, HBV, HIV) Increased risk (stronger for HCV; modest for HBV); heterogeneity across studies HCV: RR 1.84; OR 2.67. HBV: RR 1.25 (borderline) MGUS/MM Chronic antigenic stimulation; sustained immune activation; B-cell proliferation; inflammatory microenvironment; potential modulation by antiviral therapy [ [37] , [54] , [55] , [56] ] Microbiome and dysbiosis Significant association with MM risk, progression, and treatment response; supported by genetic and mechanistic data MR: taxa-specific effects (e.g., OR ∼1.7 risk-increasing; protective taxa identified); MRD-negativity associated with higher Eubacterium hallii abundance MGUS/MM; disease progression; treatment response (MRD) Chronic immune modulation; nitrogen-recycling bacteria and glutamine metabolism; inflammatory signaling; tumor-microbiome metabolic crosstalk [ [38] , [40] , [57] , 58 ] Autoimmune disease Increased risk for specific disorders Significant associations for pernicious anemia and ankylosing spondylitis MM Chronic immune activation [ [43] , [59] ] Physical activity Possible protective effect Modest association after BMI adjustment MM Immune and inflammatory regulation [ 32 ] Plant-based dietary patterns Lower MM risk association Association reported MM Anti-inflammatory and metabolic effects [ [34] , [60] , 61 ] Vitamin D deficiency Associated with poorer outcomes Association reported MM outcomes Immune and bone metabolism pathways [ 35 ] Abbreviations: MM, multiple myeloma; MGUS, monoclonal gammopathy of undetermined significance; SMM, smoldering multiple myeloma; OR, odds ratio; BMI, body mass index; T2D, type 2 diabetes. Summary of epidemiological, clinical, lifestyle, and host-related factors associated with increased or modified risk of MGUS and MM. Effect sizes are approximate and derived from representative large cohort studies and meta-analyses cited in the main text. Evidence strength reflects overall consistency across studies rather than individual reports. Reported associations for some factors are heterogeneous across populations and study designs.

Familial aggregation and inherited susceptibility

Besides environmental and clinical determinants, inherited genetic susceptibility contributes substantially to MM risk. Evidence from familial aggregation studies, rare germline variant analyses, and large-scale GWAS indicates that both high-impact pathogenic variants and polygenic risk architectures contribute to disease predisposition [ 62 , 63 ]. These data support a complex inherited susceptibility landscape.

Familial clustering and epidemiological inheritance

A substantial heritable component underlies disease susceptibility, as evidenced by consistent familial clustering patterns and the two- to four-fold increased risk observed among first-degree relatives of affected individuals [ 5 , 64 ].

Germline alterations in DNA repair and genome maintenance pathways, including genes such as TP53 and BRCA1/2 , together with immune regulatory and telomere-associated genes ( DIS3, TNFRSF13B, TREX1, POT1 , and TERT ), have been implicated in familial cases [ 65 , 66 ]. Large cohort studies further demonstrate that pathogenic germline variants in DNA repair pathways are present in approximately 9.1% of sporadic MM cases and up to 18% of familial cases [ 67 ].

Rare pathogenic germline variants

Rare pathogenic germline variants (PGVs) are detected in approximately 5–30% of patients with MM, with a higher prevalence in familial and early-onset cases, predominantly affecting genes involved in DDR, telomere maintenance, and immune regulation [ [68] , [69] , [70] ].

High- and moderate-penetrance DDR genes, including TP53 (∼2–3%), PALB2, ATM, CHEK2 , and BRCA1/2 , are identified in 4–7% of unselected cohorts and promote defective homologous recombination and impaired p53-mediated apoptosis [ 70 ], thereby accelerating genomic instability. Recurrent alterations in telomere-related genes such as POT1 and TERT , observed in approximately 2–5% of familial MM cases, promote chromosomal fragility and clonal evolution, mirroring mechanisms described in other malignancies [ 65 ]. Variants in immune and B-cell regulatory genes, including TNFRSF13B / TACI and DIS3 , occur in approximately 3–15% of patients and disrupt plasma cell maturation and immune surveillance [ [66] , [67] , [71] ] ( Table 2 ). Table 2 Germline predisposition genes and pathways associated with MM susceptibility. Table 2 dummy alt text FUNCTIONAL CATEGORY GENE(S) BIOLOGICAL FUNCTION REPORTED ASSOCIATION IN MM CLINICAL / BIOLOGICAL RELEVANCE KEY REFERENCES DDR TP53 Genome surveillance, apoptosis control Rare pathogenic germline variants reported High-impact tumor suppressor pathway [ 64 , 70 ] DDR BRCA1, BRCA2 Homologous recombination repair Pathogenic germline variants detected in MM cohorts Homologous recombination deficiency context [ 67 , 70 ] DDR ATM DNA damage signaling kinase Recurrent germline variants reported Genome stability regulation [ 70 ] DDR CHEK2 Checkpoint kinase Germline variants reported Cell-cycle checkpoint control [ 70 ] DNA REPAIR / HR SUPPORT PALB2 HR repair partner of BRCA2 Germline variants reported HR pathway integrity [ 70 ] TELOMERE MAINTENANCE POT1 Telomere protection complex Recurrent familial MM variants Telomere dysfunction, chromosomal instability [ 65 ] TELOMERE MAINTENANCE TERT Telomerase catalytic subunit Familial predisposition variants reported Replicative immortality pathways [ 65 ] IMMUNE REGULATION / B-CELL SIGNALING TNFRSF13B (TACI) B-cell survival and NF-κB signaling Germline risk variants and GWAS signals Plasma cell survival signaling [ 71 , 72 ] IMMUNE REGULATION TREX1 DNA exonuclease, immune tolerance Predisposition variants reported Immune dysregulation context [ 65 ] RNA PROCESSING / EXOSOME DIS3 RNA exonuclease Germline and somatic variants reported Plasma cell regulatory pathways [ [66] , [71] ] EPIGENETIC REGULATION EP300 Histone acetyltransferase Rare germline susceptibility variants proposed IRF4/B-cell transcriptional control [ 73 ] KINASE SIGNALING PDPK1 PI3K pathway kinase Rare splice variants reported B-cell differentiation signaling [ 73 ] GWAS RISK LOCI CDCA7L MYC-related regulatory network Reproducible GWAS locus Transcriptional regulation [ 72 , 74 ] GWAS RISK LOCI CBX7 Chromatin regulation GWAS locus at 22q13.1 Tumor suppressor regulatory context [ 72 ] GWAS RISK LOCI ULK4 Autophagy-related signaling GWAS association region Regulatory variant cluster [ 72 ] GWAS RISK LOCI RFWD3 DNA repair / replication stress GWAS locus DDR pathway linkage [ 75 ] GWAS RISK LOCI CDKN2A Cell-cycle control GWAS locus Tumor suppressor pathway [ 75 ] Abbreviations: DDR, DNA damage response; HR, homologous recombination; GWAS, genome-wide association study. Representative genes and loci implicated through familial aggregation studies, pathogenic germline variant analyses, and genome-wide association studies. Listed genes map to recurrently implicated functional pathways including DNA damage response, telomere maintenance, immune regulation, RNA processing, epigenetic control, and cell-cycle regulation. The table summarizes representative loci.

Clinically, PGVs are associated with younger age at diagnosis, increased familial cancer burden, and differential therapeutic responses, with drug-response associations reported for selected DDR alterations. Targeted enrichment strategies report higher PGV detection rates, supporting risk-adapted screening algorithms [ 68 ].

Polygenic susceptibility and GWAS architecture

Large-scale genome-wide association studies (GWAS) meta-analyses have identified >20 susceptibility loci, implicating regulatory networks in immune response, plasma cell differentiation, autophagy, apoptosis, and cell cycle control [ 76 , 77 ]. Inherited risk is predominantly polygenic, driven by cumulative low-penetrance variants [ 75 , 78 ]. Polymorphisms in telomerase-related genes, particularly TERT , further link telomere biology to susceptibility [ 79 ]. Expanded meta-analyses identified additional loci enriched in regulatory regions controlling B-cell differentiation [ 77 ].

Trans-ancestry studies identified shared susceptibility loci at 7p15.3, 17p11.2, and 22q13.1 across African and European ancestry populations, while an additional locus at 3p22.1 showed significant association exclusively in EA cohorts [ 72 ]. Functional annotation indicates enhancer regulation of CDCA7L , missense variation in TNFRSF13B/TACI , and regulatory clustering near CBX7 [ 72 , 74 ].

Polygenic risk score (PRS) models show that individuals in the highest risk quintile have a 3.44-fold increased MM risk compared with the lowest quintile [ 80 ].

Despite these promising findings, PRS are not yet clinically actionable for screening high-risk individuals or relatives in MM, and their implementation in routine practice remains constrained by several key limitations [ 81 , 82 ]. First, PRS models have not been prospectively validated in large, independent cohorts, limiting confidence in their real-world performance and clinical generalizability. In the context of MM, this limitation is particularly relevant given the relatively low disease incidence and the lack of prospective screening approaches in asymptomatic populations. In addition, predictive accuracy is reduced in ancestrally diverse populations, reflecting both the underrepresentation of non-European populations in GWAS and population-specific genetic architectures [ 83 ]. Furthermore, there are no standardized thresholds to define clinically meaningful risk categories, and it remains unclear how PRS-based stratification should be integrated with established MM risk factors such as MGUS status, family history, and cytogenetic markers. As a result, PRS should currently be considered research tools rather than validated instruments for population screening or risk assessment in relatives. Future efforts should focus on prospective validation, cross-population calibration, and integration with somatic genomic features and clinical variables to develop more robust and clinically actionable risk prediction models.

However, several limitations constrain the interpretation and translational impact of GWAS findings. Most studies are heavily biased toward populations of European ancestry, limiting generalizability and potentially exacerbating disparities in risk prediction [ 84 , 85 ]. Moreover, the effect sizes of individual susceptibility variants are modest, and the biological mechanisms linking many loci to MM pathogenesis remain incompletely defined, as many GWAS signals map to regulatory regions with context-dependent functional effects [ 86 ]. Importantly, currently identified susceptibility loci explain only a fraction of the heritable risk of MM, with large-scale interaction analyses estimating that known variants and their interactions account for approximately 17% of heritability, highlighting a substantial component of “missing heritability” [ 76 , 87 ]. Reproducibility across cohorts is also variable, reflecting differences in study design, sample size, statistical power, and phenotype definition. In addition, comparative analyses across studies reveal partial overlap in identified loci, but also inconsistencies in effect sizes and significance levels, highlighting the influence of cohort composition and analytical pipelines. Importantly, GWAS approaches primarily capture common variants and do not adequately account for rare high-impact mutations, structural variation, or epigenetic regulation, thereby providing only a partial view of inherited risk architecture. These limitations are further underscored by sequencing-based studies, which have identified rare pathogenic variants and additional layers of genetic complexity not captured by GWAS approaches [ [63] , [87] ]. Furthermore, conflicting evidence exists regarding the functional relevance of several susceptibility loci, with limited experimental validation and uncertainty about their contribution to disease biology. Although gene–gene interaction analyses have identified a limited number of interacting loci, the overall contribution of epistatic effects remains incompletely resolved, and robust replication across independent cohorts is still lacking. Gene–environment interactions are even less well characterized, representing a major unresolved area in MM susceptibility research. Addressing these challenges will require larger multi-ancestry cohorts, integrative multi-omic approaches, functional validation of candidate loci, and standardized analytical frameworks to improve robustness, biological interpretation, and clinical applicability [ 62 , 85 , 86 ].

Genetic-environment interactions

Genetic-environment interactions constitute another layer of MM etiology, where inherited susceptibility amplifies the biological impact of environmental exposures. Rare germline variants affecting DNA damage response, telomere maintenance, and immune pathways may increase cellular vulnerability to genotoxic stressors [ 67 ].

Occupational exposure to pesticides and solvents has been associated with increased MM risk, and may exert stronger biological effects in individuals carrying germline alterations in DNA repair pathways [ 27 ]. Germline alterations in DNA repair genes and immune regulators increase cellular sensitivity to genotoxic stressors including pesticides, solvents, and benzene, accelerating mutational accumulation, inflammatory signaling, and clonal evolution during progression from MGUS and SMM to MM [ 88 , 64 ]. Obesity may further synergize with polygenic risk through chronic inflammation and metabolic reprogramming of the bone marrow microenvironment [ 89 ]. In genetically predisposed individuals, these inflammatory and metabolic pressures may facilitate earlier clonal expansion and disease progression.

High-resolution longitudinal cohort efforts integrating exposure data, clonal hematopoiesis dynamics, and molecular profiling further support the role of G × E interactions in shaping disease trajectories and provide a foundation for personalized prevention and surveillance strategies in high-risk populations [ 90 ].

Collectively, these findings support a model in which germline susceptibility not only influences overall disease risk but may also shape the evolutionary trajectories of plasma cell neoplasia [ [64] , [91] ]. In particular, inherited alterations affecting DNA repair, telomere maintenance, and immune regulation may create permissive genomic backgrounds that shape the spectrum of primary somatic events, such as IgH translocations or hyperdiploid karyotypes [ 88 ]. Although direct mechanistic evidence in MM remains limited, emerging integrative genomic data suggest that germline-driven differences in genomic stability and mutational processes may influence the timing, type, and selection of early driver events. Future studies integrating germline variation with longitudinal somatic profiling will be essential to define how inherited risk architectures constrain clonal initiation and shape subsequent evolutionary pathways.

Somatic genomic landscape of MM

Primary cytogenetic abnormalities represent early detectable genomic events in MM and define major biological subtypes. These lesions arise during premalignant stages, persist throughout clonal evolution, and shape downstream mutational trajectories, clinical behavior, and therapeutic vulnerability. Two largely mutually exclusive categories account for most cases: recurrent immunoglobulin heavy chain (IgH) chromosomal translocations and hyperdiploidy. Together, these categories define distinct evolutionary routes and underpin cytogenetic risk stratification.

Primary cytogenetic categories

Primary cytogenetic events define early MM subtypes and shape genomic evolution.

IgH-driven translocations

• IgH translocations as early genomic drivers and their clinical detection

Chromosome 14q32 rearrangements involving the IgH locus represent one of the most frequent and relevant primary genomic events in MM [ 92 ]. These alterations arise early during disease progression, are detectable in MGUS, and generally persist during progression, while secondary alterations such as MYC activation and TP53 loss accumulate later [ 93 ].

At the functional level, IgH translocations juxtapose oncogenes to the highly active IgH enhancer region, leading to deregulated expression of genes involved in cell cycle control, epigenetic regulation, transcriptional programs, and survival. IgH rearrangements are detected in approximately 40–50% of NDMM patients using conventional techniques [ 94 , 95 ], and may approach 50–60% with comprehensive genomic profiling. • Dominant IgH translocation subtypes and biological consequences

Four chromosomal partners account for the majority of primary IgH translocations: CCND1 (11q13), CCND3 (6p21), FGFR3/WHSC1 (MMSET) (4p16), and MAF (16q23) [ 96 ].

The t(11;14)(q13;q32), present in approximately 15–20% of patients [ 97 , 98 ], drives CCND1 overexpression [ 99 ] and is associated with a lymphoplasmacytic phenotype, frequent CD20 expression, and relative biological indolence [ 100 ]. This subgroup is generally classified as standard risk [ 101 ]. Tumors with t(11;14) show relative dependence on BCL2-mediated survival pathways, which is associated with increased response rates to venetoclax-based strategies in selected patient subsets [ 102 ].

The t(4;14)(p16.3;q32), observed in approximately 10–15% of cases [ 103 ], results in simultaneous activation of FGFR3 and WHSC1 (MMSET) [ 103 ]. MMSET overexpression alters chromatin methylation and DNA damage response pathways [ 104 ], while FGFR3 contributes oncogenic signaling in a subset of patients harboring activating mutations, detected in approximately 17% of t(4;14)-positive tumors [ 98 ]. This subgroup defines a high-risk entity characterized by aggressive clinical behavior, frequent renal involvement, and inferior survival [ 92 ]. PI-based therapy partially mitigates adverse prognosis, highlighting the therapeutic relevance of molecular stratification [ 105 ].

The t(14;16)(q32;q23), present in approximately 2–3% of patients [ 106 ], induces MAF overexpression and has been associated with inferior survival [ [106] , [107] ]. MAF activation promotes APOBEC3A and APOBEC3B expression, generating a hypermutator phenotype characterized by APOBEC mutational signatures and increased genomic instability [ 108 ].

Less frequent primary events include t(6;14) (1–2%), associated with CCND3 activation and generally neutral prognosis [ 109 ], and t(14;20) (∼1%), associated with MAFB activation and adverse prognosis in overt MM [ 110 ].

Hyperdiploidy

In parallel to IgH-driven disease, hyperdiploidy represents the second major primary cytogenetic category in MM and accounts for approximately 45–50% of NDMM cases in Western populations [ 111 , 112 ]. It is defined by non-random gain of odd-numbered chromosomes, most commonly involving chromosomes 3, 5, 7, 9, 11, 15, 19, and 21. These gains are acquired in structured evolutionary sequences rather than through random aneuploidy, defining a distinct early oncogenic route.

Reported incidence varies across cohorts, likely reflecting technical and population differences [ 111 , 112 ]. Among patients with cytogenetically abnormal karyotypes, hyperdiploidy is the most frequent numerical pattern, occurring in approximately 46–68% of cases, compared with pseudodiploid or hypodiploid configurations.

From a clinical perspective, patients with abnormal metaphase karyotypes exhibit inferior survival outcomes compared with those with normal karyotypes [ 113 ]. Hyperdiploid MM is associated with more favorable survival compared with hypodiploid disease, defined by fewer than 45 chromosomes [ 114 ], indicating that numerical chromosomal patterns differ in impact.

Prognostic impact is heterogeneous. Patients with hyperdiploidy plus high-risk cytogenetic abnormalities, including del(17p13), t(4;14), and gain of 1q21, experience significantly worse survival outcomes than patients with isolated hyperdiploidy [ 115 ]. Chromosome-specific effects further modulate prognosis within the hyperdiploid subgroup. Among hyperdiploid patients, only trisomy 3 and trisomy 5 were independently associated with improved OS, whereas trisomy 21 correlated with adverse clinical outcomes [ 116 , 117 ], indicating that trisomy pattern, not chromosome count alone, influences prognosis.

Secondary genomic events and progression-associated events

Secondary genomic events shape MM progression biology and include chromosome-arm aneuploidy, focal CNVs, and secondary structural alterations that accumulate on primary cytogenetic backgrounds.

Aneuploidy-driven/trigger progression in MM

With the advent of high-resolution molecular cytogenetic techniques, studies show that 95–100% of patients with MM harbor abnormal karyotypes, often characterized by highly complex combinations of structural and numerical alterations.

Early comparative genomic hybridization and FISH-based studies established that these alterations are nonrandom and biologically selected. Reported recurrent chromosome-arm gains involving 19p, 9q, 11q, 12q, 15q, 17q, and 22q, together with deletions affecting 6q and 16q. Subsequent interphase FISH analyses confirmed chromosomal aberrations in up to 86% of NDMM patients, with frequent gains of chromosomes 3, 5, 7, 9, 11, 15, and 21 and losses involving chromosomes 13 and 17, and identified del(13q14) and IGH translocations as major prognostic markers [ 118 ].

gain

At the chromosome-arm level, gain of 1q is the most frequent and clinically relevant numerical event. It occurs in approximately 35–40% of NDMM patients and increases in frequency at relapse, where it strongly correlates with treatment resistance and inferior survival [ [115] , [119] ].

Prognostic impact appears to be copy-number dependent, with ≥4 copies generally associated with inferior outcomes compared with three copies, although this association is influenced by co-existing cytogenetic abnormalities and contemporary treatment strategies [ 120 ]. Notably, the prognostic effect of +1q/1q21 gains is not uniform across studies: in thalidomide-based regimens no significant survival impact has been reported, and in other series the adverse association observed in univariate analyses did not retain independent prognostic value in multivariable models [ 121 ].

deletion

Deletion of chromosome 1p is detected in approximately 27–30% of patients and represents another major adverse lesion [ 122 ]. del(1p) is independently associated with poor prognosis [ 116 , 123 , 124 ]. Of particular biological relevance are deletions involving 1p12 and 1p32.3, regions that harbor critical tumor suppressor candidates.

del(13q)

Deletion of chromosome 13q occurs in approximately 50–60% of patients [ [124] , [125] ]. Its adverse prognostic signal largely reflects co-occurrence with other high-risk lesions.

del(17p)

Deletion of chromosome 17p involving the TP53 represents the most clinically impactful arm-level abnormality. Del(17p) is detected in approximately 8–10% of NDMM patients and is consistently associated with inferior PFS and OS [ 126 , 127 ]. Its prevalence increases dramatically during disease progression, reaching very high frequencies in advanced and relapsed stages [ 128 , 129 ]. TP53 functions as a central regulator of cell-cycle arrest, DNA repair, and apoptosis, and 17p loss is associated with aggressive disease, inferior OS, and increased risk of extramedullary involvement [ [111] , [124] 130 , 131 ].

Other arm-level gains/losses

Additional recurrent arm-level gains include gains of 8q, 9q, and 11q, with 8q gain of particular relevance due to the MYC locus at 8q24 [ [124] , [130] , [132] ]. Recurrent arm-level losses include 1p, 6q, 8p, 9q, 13q, 16q, and 17p, contributing to tumor suppressor gene depletion and genomic imbalance [ [124] , [125] , 133 ]. Several of these losses co-occur with gain(1q), defining broader chromosomal instability patterns.

MYC secondary events

Dysregulation of MYC represents a central secondary genomic event in MM progression [ 134 ]. Unlike primary IgH translocations, MYC rearrangements are rare in premalignant stages (3–4%) [ 135 ], but increase in frequency in NDMM (approximately 15–20%) [ 110 , [135] , [136] ] and become further enriched at relapse, where they are associated with proliferative transcriptional programs and inferior clinical outcomes [ 130 , 136 ].

Recurrent MYC rearrangements, focal gains, and amplifications involving 8q24 constitute multiple structural mechanisms converging on MYC activation [ 130 , 136 ]. These alterations are particularly enriched in tumors with homologous recombination deficiency and show an inverse correlation with t(4;14) [ [135] , [136] ]. Their frequent coexistence with primary IgH translocations supports a multistep evolutionary model of myeloma progression.

Functionally, MYC activation drives proliferative transcriptional programs contributing to clonal diversification, adverse clinical behavior and progression [ [130] , [134] ]. The stepwise increase in MYC alterations from MGUS to symptomatic and relapsed MM reinforces its role as a progression-associated lesion rather than an initiating event.

Driver-focused copy number remodeling

Building on the arm-level architecture described above, focal CNVs modulate oncogenic dosage and functional heterogeneity without defining new cytogenetic categories.

Recurrent amplifications and deletions target gene-dense regions that modulate key oncogenic pathways. Representative events include focal amplification of CKS1B (1q21) and MYC (8q24), deletions involving TP53 (17p13), CDKN2C and FAF1 (1p32), RB1 (13q14), CYLD (16q12), and BIRC2/BIRC3 (11q22), converging on cell-cycle regulation, DNA damage response, and NF-κB signaling [ 130 , 136 ].

Focal CNVs interact with aneuploid backgrounds to promote transcriptional reprogramming, clonal fitness, and therapeutic resistance. Integrated analyses associate specific CNV patterns with aggressive transcriptional states and inferior survival (Gupta et al., 2019).

Longitudinal studies further show CNV remodeling under therapeutic pressure, fostering branched clonal evolution and resistance [ 130 ]. CNV remodeling frequently coexists with complex structural variation, reinforcing genomic instability and hierarchical evolution [ 137 ] ( Fig. 1 ). Fig. 1 Integrated Genomic Architecture and Hierarchical Evolution of MM. Primary IgH translocations and hyperdiploidy define early MM biological subtypes. Secondary cytogenetic changes and CNV remodeling add arm-level and focal gains and deletions affecting key oncogenes and tumor suppressors. Somatic mutations and structural variants further deregulate signaling and genome control pathways. These alterations accumulate through branching clonal evolution from precursor states to relapsed disease under therapeutic pressure. Fig 1 dummy alt text

Genomic detection strategies: from FISH to NGS and optical genome mapping (OGM)

Accurate identification of myeloma genomic abnormalities is central for risk stratification and therapeutic decision-making. Conventional diagnostic workflows rely on fluorescence in situ hybridization (FISH), SNP arrays, targeted sequencing, and OGM, typically requiring plasma cell enrichment for optimal sensitivity.

Conventional cytogenetics and FISH

Conventional metaphase cytogenetics and interphase FISH remain central to MM diagnosis and risk stratification, forming the backbone of current clinical guidelines. FISH, performed on plasma cell-enriched samples, overcomes the low proliferative index that limits metaphase analysis and reliably detects high-risk lesions such as t(4;14), t(14;16), del(17p), 1q gain, and 1p deletion [ 138 ]. Detection rates consistently exceed those of conventional cytogenetics: abnormalities were identified in 49.2% versus 16.9% of cases in one cohort [ 139 ], and up to 97% versus 30% when FISH combined with array-CGH was compared with karyotyping alone [ 140 ].

Beyond detection, FISH allows estimation of lesion burden and subclonal distribution, guiding risk-adapted therapy. Conventional cytogenetics, although less sensitive, retains complementary value by providing a global view of ploidy and chromosomal complexity, including hyperdiploidy and complex karyotypes [ 141 ].

NGS-based structural variant detection

NGS extends structural variant detection beyond predefined probe panels. Whole-genome sequencing (WGS) and targeted sequencing approaches can identify cryptic rearrangements, resolve complex breakpoints, and uncover novel fusion partners [ 135 ]. Panel design remains critical, as limited genomic coverage may underestimate clinically relevant alterations, including certain MYC rearrangements.

Beyond structural rearrangements, NGS enables simultaneous detection of single-nucleotide variants (SNVs) and small insertions/deletions (indels), particularly relevant for TP53 , where focal deletions frequently coexist with inactivating mutations.

NGS sensitivity depends on sequencing depth and variant allele fraction; high-depth sequencing achieves >98% sensitivity for SNVs and indels [ 142 ]. Integrated WES/RNA-seq demonstrate complete concordance with FISH for CNAs while additionally resolving translocation breakpoints and mutations [ 143 ]. High-performance custom panels also show >99% sensitivity for IGH translocations and CNVs [ 144 ].

Collectively, these findings support integrated NGS-cytogenetic strategies, as NGS captures structural variants and sequence mutations, providing gene-level profiling beyond conventional cytogenetics.

Optical genome mapping (OGM)

Optical Genome Mapping (OGM) enables detection of structural variants, CNVs, and aneuploidy in a single experiment, overcoming the locus restriction of targeted FISH panels. Across clinical cohorts, OGM identifies canonical abnormalities detectable by FISH while revealing additional structural variants. One study reported a ∼30% increase in prognostic yield compared with FISH [ 145 ], whereas another detected additional alterations in up to 60% of cases, including chromoanagenesis events [ 146 ]. High concordance with FISH for variants ≥5% with a variant allele frequency supports reliability for major clonal events [ 147 ].

By capturing balanced rearrangements and numerical changes in a single workflow, OGM functions as a high-resolution digital karyotype and may consolidate multiple cytogenetic assays. However, OGM sensitivity decreases for low-level subclones (15–20%) and it cannot reliably resolve centromeric or telomeric rearrangements [ 148 ]. Performance may be reduced in samples with low plasma cell purity or highly subclonal populations. In addition, OGM requires ultra-high molecular weight DNA, which can be challenging to obtain from bone marrow samples. Importantly, OGM does not detect SNVs or small insertions/deletions [ 149 ], and therefore requires integration with sequencing platforms for comprehensive profiling.

Prognostic implications and therapeutic modulation

Cytogenetic abnormalities are the strongest predictors of clinical outcome. Deletion 17p, t(4;14), and 1q21 gain are independent adverse prognostic factors for PFS, with del(17p13) also predicting inferior OS [ 112 ]. Cytogenetic complexity further worsens prognosis.

High-dose therapy followed by autologous stem cell transplantation improves response depth but does not fully offset high-risk cytogenetic features. In contrast, incorporation of PI shows particular benefit in t(4;14) and 1q21 gain disease, supporting molecularly risk-adapted therapy [ 97 , 105 ].

Mutational profiles and clonal evolution

High-resolution chromatin conformation studies show that CNV breakpoints frequently occur at the boundaries of topologically associating domains (TADs), suggesting preferential disruption of regulatory regions. TADs organize the genome into insulated neighborhoods that constrain enhancer-promoter interactions and shape gene expression programs [ 150 ]. TAD disruption alters enhancer-promoter interactions and contributes to oncogenic dysregulation [ 151 ]. Compared with normal B cells, MM shows increased TAD number, reduced average TAD size, and A/B compartment switching in ∼20% of genomic regions, consistent with higher-order genome reorganization [ 152 , 153 ] ( Fig. 2 ). Fig. 2 Cytogenetic evolution and hierarchical acquisition of alterations in MM progression. Abbreviations: MM, multiple myeloma; MGUS, monoclonal gammopathy of undetermined significance; SMM, smoldering multiple myeloma; HRD, homologous recombination deficiency; IgH, immunoglobulin heavy chain; del, chromosomal deletion; amp, amplification; t, chromosomal translocation; NRAS , neuroblastoma RAS viral oncogene homolog; KRAS , Kirsten rat sarcoma viral oncogene homolog; BRAF, B-Raf proto-oncogene serine/threonine kinase; CCND1 , cyclin D1; DIS3 , DIS3 homolog exosome endoribonuclease; IRF4 , interferon regulatory factor 4; FGFR3 , fibroblast growth factor receptor 3; PRDM1 , PR domain zinc finger protein 1 (BLIMP1); MYC, MYC proto-oncogene. In the non-hyperdiploid group, mutation labels are color-coded according to the associated IgH translocation subtype, as indicated in the right-hand color legend. Fig 2 dummy alt text

Comparative genomic studies indicate that MGUS is genetically related to MM but exhibits a lower burden of genomic abnormalities. The frequency of chromosomal gains and losses, including hyperdiploidy, 1q gain, and del(13q), is reduced in MGUS, supporting a stepwise accumulation of genomic instability during progression. MGUS also shows a less complex somatic mutation spectrum that correlates with chromosomal and mutational burden [ 154 ]. MYC translocations and TP53 lesions are typically absent in MGUS, supporting their role as transformation-associated events.

Large sequencing cohorts demonstrate that progression is accompanied by diversification of mutational processes affecting both coding and non-coding regions. Analysis of CoMMpass whole-exome and whole-genome datasets identified recurrent SNVs, structural rearrangements, and characteristic mutational signatures linked to endogenous enzymatic activity and therapy-related mutagenesis [ [91] , [155] ]. Integrated genomic analyses combining sequencing, CNV profiling, and cytogenetics describe linear, branching, and parallel evolutionary trajectories, highlighting dynamic clonal architecture [ [91] , [137] ].

In MM, subclonal architecture can be reconstructed from bulk NGS data using probabilistic frameworks based on Bayesian clustering of variant allele frequencies and copy-number–adjusted cancer cell fractions. These approaches model the contribution of individual mutations to the overall tumor population, accounting for tumor purity and local copy number, and group variants into clusters that reflect distinct clonal populations. In particular, Dirichlet-process-based models enable data-driven inference of the number and composition of subclonal populations without requiring prior specification of cluster number [ 152 , 156 ]. This allows the identification of phylogenetically related clones and the distinction between truncal and branch mutations, providing a basis for reconstructing tumor evolutionary histories. These approaches have been applied in longitudinal and progression settings to track clonal dynamics over time and under therapeutic pressure, revealing that subclonal diversity often precedes clinical transformation and that relapse may arise from pre-existing minor clones [ [152] , [157] , 158 ]. However, despite their relevance, their implementation in routine clinical practice remains limited by the lack of standardized bioinformatic pipelines, methodological variability across tools, and the intrinsic constraints of bulk sequencing data. The development of robust and standardized analytical approaches will be essential to fully integrate subclonal resolution into precision oncology strategies.

An additional layer of complexity arises from allele-specific expression of somatic variants. Transcriptomic profiling shows that mutant alleles are variably expressed, with selective activation or silencing that modulates functional impact beyond DNA sequence alone [ 159 ].

At the pathway level, somatic mutations are enriched in receptor tyrosine kinase signaling and cell adhesion networks. Sequencing of tumors and myeloma cell lines identified recurrent alterations affecting plasma cell adhesion, migration, and microenvironmental interactions [ 160 ].

Germline susceptibility variants may further modulate the risk and molecular progression of plasma cell dyscrasias. Rare inherited EP300 variants have been proposed to contribute through IRF4 pathway dysregulation, increased BCL6 activity, and reduced p53 signaling [ [73] , [74] , [88] , 108 ]. Additional candidate variants include PDPK1 splice-site alterations affecting immunoglobulin recombination and transcription regulators. Several candidate risk variants cluster at 22q13 near APOBEC -family loci and are consistently associated with APOBEC-related mutagenesis in MM evolution [ [74] , [112] ].

At relapse, genomic complexity increases. Integrative analyses show more chromosomal imbalances at relapse than at diagnosis, driven mainly by additional losses, with recurrent relapse-associated gains on 8q and losses of 10q [ 137 , [158] , [161] ].

Large CNVs (>100 Mb) exert stronger transcriptional effects than smaller alterations and preferentially deregulate cytokine-related and integrin signaling pathways. Relapse-associated expression changes include activation of PRAME (22q11) and BOP1 (8q24). Global DNA methylation patterns remain relatively stable between diagnosis and relapse, indicating a dominant role for chromosomal remodeling over epigenetic reprogramming in late-stage progression [ 161 ].

Germline genetic susceptibility

GWAS

As mentioned above, GWAS have established that MM has a substantial inherited component driven by a polygenic architecture. Numerous common low-penetrance variants collectively contribute to disease susceptibility. Large-scale meta-analyses indicate that germline risk loci explain approximately 20–25% of heritable risk, confirming that genetic predisposition arises from the cumulative effect of multiple regulatory variants rather than rare high-penetrance alleles. GWAS therefore provide direct molecular evidence of inherited susceptibility beyond familial aggregation. International collaborations such as IMMEnSE were instrumental to power discovery and cross-cohort validation [ 162 ].

A landmark meta-analysis conducted by Mitchell et al. analyzed 9866 cases and 239,188 controls. This study validated nine previously reported susceptibility loci and identified eight additional risk regions. Newly implicated loci included JARID2 (6p22.3), ATG5 (6q21), SMARCD3 (7q36.1), CCAT1 (8q24.21), CDKN2A (9p21.3), WAC (10p12.1), RFWD3 (16q23.1), and PREX1 (20q13.13) [ 75 ]. Earlier GWAS also identified additional loci, including a variant at 2q12.3 associated with increased MM risk and replicated across multiple cohorts [ 163 ].

Trans-ethnic GWAS approaches further refined inherited MM risk architecture by leveraging ancestry-specific linkage disequilibrium (LD) structure, which is particularly relevant given higher incidence in individuals of African ancestry. Rand et al. combined European and African ancestry cohorts and replicated several established European risk loci at CDCA7L (7p15.3), TNFRSF13B (17p11.2), and CBX7 (22q13.1). They also improved fine-mapping of shared susceptibility regions using ancestry-specific LD patterns [ 72 ]. The expanded African ancestry meta-analysis by Du et al. showed concordant risk architecture across populations while identifying ancestry-dependent effect sizes [ 164 ]. More recently, Went et al. reported that multi-ancestry PRS identify the highest predicted genetic risk in admixed American populations, partly driven by enrichment of risk alleles at loci such as 3p22.1 near ULK4 [ 165 ].

Beyond single-locus associations, pathway-based and interaction analyses reveal higher-order genetic dependencies. Polygenic interaction modeling of two independent GWAS datasets identified 16 significant interacting variant pairs, supporting a contribution of epistatic effects to inherited MM risk [ 76 ]. Independent replication studies further confirmed additional susceptibility loci, including variants at 8q24, 7p15.3, 3p22.1, and 2p23.3 [ [166] , [167] , [168] ].

Functionally, many GWAS risk variants localize to non-coding regulatory regions and act through expression quantitative trait loci (eQTL) mechanisms in hematopoietic tissues. Risk loci cluster near genes involved in plasma cell differentiation and immune signaling, including TNFRSF13B / TACI and PREX1 . Integration of transcriptome-wide association studies (TWAS) has prioritized >100 candidate susceptibility genes, including APOBEC -family members, linking inherited risk to mutational processes relevant to somatic progression in MM. Importantly, aggregation of GWAS signals into PRS enables population-level susceptibility stratification, with individuals in the highest percentiles exhibiting a significantly increased relative risk. Trans-ethnic modeling improves PRS portability across ancestries and supports more equitable risk prediction strategies.

Candidate gene association studies

Candidate gene association studies have evaluated inherited variants in pathways related to drug transport, DNA repair, autophagy, immune regulation, plasma cell differentiation, microenvironmental signaling and telomere maintenance. Reported associations span susceptibility, clinical features, treatment outcomes and comorbidities [ [79] , [166] , 169 , 170 ] ( Table 3 ). Table 3 Candidate-gene polymorphisms associated with MM susceptibility and treatment outcome. Table 3 dummy alt text Functional domain Gene Variant / haplotype Reported association type Reported effect Key references Xenobiotic transport GSTM1, GSTT1 Gene deletions Treatment outcome No significant association with response or survival after VAD or high-dose melphalan [ 171 ] Xenobiotic metabolism GSTP1 Val/Val genotype Treatment outcome Improved outcome with conventional chemotherapy [ 172 ] Drug transport ABCB1 (MDR1) C1236T, C3435T Treatment outcome T alleles associated with longer PFS; no OS association [ 173 ] Drug transport ABCB1 rs2235013 Survival Associated with survival differences [ 174 ] Drug transport ABCC2 rs4148388 Survival Associated with survival differences [ 174 ] Drug transport ABCB1 rs1045642 Susceptibility No association with MM risk (meta-analysis) [ 175 ] Drug transport ABCB1, ABCG2, ABCC1, ABCC2, ABCC3 Multiple SNPs Susceptibility Association with MM risk in candidate-gene case–control study [ 176 ] IMiD target regulation CRBN rs1714327, rs1705814; rs6768972, rs1672753 (promoter SNPs) Treatment outcome (lenalidomide and thalidomide-based regimens) Minor alleles associated with lower probability of ≥PR and shorter PFS in RRMM; promoter variants independently associated with PFS and OS in CTD-treated patients; additional CRBN polymorphisms associated with differential IMiD response [ [177] , [178] , [179] ] Inflammasome regulation CARD8 rs2043211 (C10X) Susceptibility / clinical features AT genotype associated with increased MM risk and immune-related features [ 180 ] Inflammatory signaling NF-κB rs28362491 (−94 ins/del ATTG) Clinical features Associated with higher tumor burden parameters [ 180 ] Cytokine regulation IL18 rs16944 Clinical features Associated with hematologic and tumor burden parameters [ 180 ] Cytokine network IL6, IL1, IL10 Multiple SNPs Susceptibility Associated with MM risk in meta-analyses [ 181 ] Plasma cell differentiation ELL2 rs6877329 Susceptibility / biology Reduced enhancer activity and ELL2 expression; MM risk locus [ [55] , [182] , 183 ] Telomere maintenance TERT pathway genes Common polymorphisms Susceptibility Associated with MM risk and telomere length variation [ 79 ] Angiogenesis VEGF rs699947 / rs833061 / rs2010963 (ACG haplotype) Treatment outcome Shorter time to thalidomide failure [ 184 ] Angiogenesis VEGF rs1570360 (−1154G>A) Biology / prognosis Associated with higher marrow microvessel density [ 185 ] Angiogenesis VEGF, VEGFR2 Multiple SNPs / haplotypes Treatment outcome Associated with response and time to treatment failure [ 185 ] Class switch recombination (CSR) CSR pathway genes Multiple SNPs Susceptibility and survival Genetic polymorphisms in CSR genes associated with MM risk and survival in IMMEnSE [ 186 ] Antigen presentation HLA-DRB1 DRB1×01:01 tagged allele Susceptibility GWAS risk signal [ 187 ] Antigen presentation HLA-C, HLA-B C07:02 g, B07:02 g Susceptibility Associated with MM risk [ 187 ] NK regulation HLA/KIR combinations C1−/C2+/Bw4+, KIR ligand pairs Susceptibility Associated with MM occurrence and NK licensing patterns [ 188 , 189 ] Transcriptional regulation HNF1B; outcome-related eQTL loci; integrative genomic risk loci Common variants; eQTL SNPs; germline variants incorporated in genomic risk models Survival Associated with overall survival in MM (IMMEnSE); regulatory variants influencing gene expression predict survival differences; integrative analyses confirm germline contribution to clinical outcome heterogeneity [ [170] , [190] , [191] , [192] ] Post-transcriptional regulation (miRNA binding variants) Multiple target genes (miRSNPs) miRNA-binding site polymorphisms Susceptibility miRSNPs associated with altered MM risk in IMMEnSE cohorts [ 193 , 194 ] Abbreviations: PFS, progression-free survival; OS, overall survival; VAD, vincristine-doxorubicin-dexamethasone; HLA, human leukocyte antigen; KIR, killer cell immunoglobulin-like receptor; NK, natural killer. Representative associations reported in candidate-gene and pharmacogenetic studies, grouped by functional pathway. Reported effects include disease susceptibility, clinical features, treatment response, or survival endpoints. Findings are heterogeneous across cohorts and treatments.

Xenobiotic transport and drug metabolism (ABC, GST)

ATP-binding cassette (ABC) transporters are key xenobiotic efflux pumps that contribute to multidrug resistance in MM. They export chemotherapeutic agents from malignant plasma cells, thereby reducing intracellular drug accumulation. Major transporters implicated include ABCB1 (P-glycoprotein/ MDR1 ), ABCC1 ( MRP1 ), ABCC5 ( MRP5 ), and ABCG2 ( BCRP ), frequently upregulated in relapsed and refractory disease. Increased transporter expression is frequently driven by epigenetic demethylation and microenvironment-derived signaling pathways, including resistin-mediated NF-κB activation. These mechanisms collectively promote cross-resistance.

Polymorphisms in xenobiotic transport and detoxification genes have been investigated as modifiers of treatment response and outcome. Early studies reported that GSTM1 and GSTT1 deletions did not significantly influence response or survival in NDMM patients treated with vincristine, doxorubicin, and dexamethasone (VAD) or high-dose melphalan [ 171 ]. Similarly, Schilthuizen et al. analyzed CYP3A4, CYP3A5, MDR1, GSTM1 , and GSTT1 variants in 209 NDMM patients and found no significant associations with outcome or toxicity [ 195 ]. In contrast, data from the MRC VII trial showed that the GSTP1 Val/Val genotype was associated with improved outcome in patients receiving conventional chemotherapy-based regimens [ 172 ], suggesting context-dependent pharmacogenetic effects.

Genetic variation in MDR1 (ABCB1) has also been associated with differential outcomes. Xiao et al. reported that the T allele at C1236T was associated with longer PFS than CC homozygotes, and the T allele at C3435T was associated with longer PFS than CC, with no significant OS association [ 173 ].

Larger multicenter analyses from the IMMEnSE consortium identified survival associations for ABCB1 and ABCC2 variants [ 174 ], supporting a role of transporter polymorphism in therapy modulation rather than baseline susceptibility [ 176 ]. ABCC2 overexpression has also been linked to increased drug clearance and resistance [ 174 ]. In contrast, a meta-analysis of MDR1 rs1045642 (C > T) across four case-control studies found no association with disease susceptibility, consistent with a predominant role in therapy modulation rather than baseline risk [ 175 ].

DNA damage response and genomic stability

Genomic instability is a defining feature of MM and reflects alterations in DDR pathways that promote clonal evolution and therapeutic resistance. Both intrinsic repair defects and microenvironmental modulation contribute to altered DDR dynamics [ 196 , 197 ]. Bone marrow niche-derived signals modulate DDR activation and repair capacity, reinforcing instability and therapy resistance [ 197 ].

DDR signaling is frequently compromised. Alterations in checkpoint regulators such as ATM and TP53 blunt apoptotic responses, allowing unstable clones to persist [ 196 ]. Compensatory activation of specific repair modules may further sustain survival under genotoxic stress [ 196 ]. The microenvironment amplifies instability through hypoxia, oxidative stress, inflammatory cytokines, and stromal-derived factors that increase DNA damage and influence DDR pathway choice [ 198 ]. Epigenetic regulators also modulate DDR control. SIRT6 regulates DNA damage signaling and genome stability, and elevated expression correlates with adverse outcomes and resistance to DNA-damaging agents [ 199 ].

Immune regulation and inflammatory pathways (NLRP3, NF-κB, cytokines)

Dysregulation of the NLRP3 inflammasome pathway has been described in MM, with reduced NLRP3 and caspase-1 in marrow plasma cells accompanied by elevated circulating IL-18 and altered sRANKL/OPG ratios [ 200 ]. Inflammasome activation also extends beyond malignant plasma cells, as β2-microglobulin induces NLRP3 activation in tumor-associated macrophages, increasing IL-1β/IL-18 and promoting progression [ 201 ].

Polymorphisms in inflammasome-related genes, particularly CARD8 - C10X (rs2043211), have been associated with MM susceptibility and immune-related clinical characteristics [ 180 ], while variants in NF-κb1 (rs28362491) and IL-18 (rs16944) correlated with tumor burden parameters [ 180 ]. Meta-analyses further report associations between IL-6, IL-1 , and IL-10 polymorphisms and MM susceptibility [ 181 ]. Although peripheral T helper subsets are implicated in MM biology, associations with inflammasome polymorphisms appear limited [ 180 ], indicating that inflammatory dysregulation may be compartment-restricted.

Plasma cell differentiation and transcriptional regulation (ELL2, IRF4 axis)

Functional characterization of germline susceptibility loci provides mechanistic insight into inherited risk. Li et al. identified rs6877329 as a functional variant at 5q15, showing disruption of a B cell-specific enhancer ∼35 kb upstream of ELL2 that loops to the ELL2 promoter [ 55 ]. The rs6877329-C risk allele reduces enhancer activity and was associated with decreased ELL2 expression in CD138⁺ plasma cells, supported by eQTL analyses, and this effect was lineage-restricted with no detectable effect in non-plasma cell compartments.

Ali et al. confirmed that the 5q15 risk allele lowers ELL2 expression across populations and associates with increased ribosomal gene expression [ 182 ]. ELL2 regulates terminal B-cell differentiation and immunoglobulin production as part of the super-elongation complex. Park et al. showed that ELL2 drives secretory mRNA production and activates the unfolded protein response (UPR) in plasma cells [ 202 ]. Reduced ELL2 expression therefore links regulatory variation to impaired differentiation and risk of transformation. ELL2 interfaces with core plasma-cell transcriptional programs, and its perturbation may favor differentiation states that support clonal persistence [ 202 ].

Angiogenesis and microenvironment modulators (VEGF/VEGFR)

Angiogenesis contributes to MM pathogenesis and prognosis, and increased marrow microvessel density is associated with unfavorable outcomes, supporting the rationale for targeting vascular endothelial growth factor (VEGF) signaling [ 203 ].

Genetic variation within the VEGF signaling axis modulates angiogenic activity and response to thalidomide-based regimens. The ACG haplotype composed of rs699947, rs833061, and rs2010963, associated with increased VEGF production, was linked to shorter time to thalidomide failure in relapsed MM and shorter time to next treatment in thalidomide-treated NDMM cohorts [ 184 ].

At the tissue level, VEGF rs1570360GG carriers showed higher bone marrow microvessel density [ 185 ]. Polymorphisms in VEGF and VEGFR2 have been associated with treatment response and with disease risk and aggressiveness in case-control analyses [ 204 ]. Genotypes associated with higher angiogenic signaling were linked to increased relapse or progression risk independent of ISS or transplant status [ 185 ].

HLA polymorphisms and immune surveillance

Human leukocyte antigen (HLA) molecules mediate immune surveillance by presenting antigenic peptides to T-cell receptors. HLA class I/II polymorphisms shape peptide-binding repertoires and influence immune recognition thresholds [ 205 ]. In MM, inherited HLA variation has been implicated in disease susceptibility and immune control [ 187 ].

GWAS identified HLA-DRB501 as risk allele tagging the classical HLA-DRB101:01 [ 187 ]. In addition, HLA-C07:02 g and HLA-B07:02 g were also reported as independent susceptibility factors, supporting contributions from both class I and class II molecules. HLA class I variation may also influence NK-cell regulation through KIR interactions, thereby influencing immune surveillance [ 189 ].

Beelen et al. reported that a C1-/C2+/Bw4+ epitope combination was associated with MM occurrence, with increased Bw4/KIR3DL1 and C2/KIR2DL1 genotype co-occurrence in MM patients [ 188 ]. Martínez-Sánchez et al. reported that MM risk is associated with less diverse and lower-affinity KIR-HLA-C licensing interactions, a pattern associated with reduced NK responsiveness and potential immune escape [ 189 ].

Therapeutically, inhibitory NK checkpoints restrain anti-myeloma activity, and targeting these pathways may enhance NK cytotoxicity [ 206 ]. Collectively, inherited HLA/KIR variation shapes MM susceptibility and immune control by modulating NK-cell education and effector function.

Genetic determinants of therapeutic response in MM

Genetic determinants of response to PIs

PIs inhibit the proteolytic core of the ubiquitin-proteasome system (UPS), a central regulator of protein turnover and signaling homeostasis. By blocking UPS-dependent degradation, proteins accumulate, inducing proteotoxic stress and activation of terminal UPR. This mechanism is particularly relevant in malignant plasma cells, where high immunoglobulin production and genetically driven secretory load create substantial proteostasis stress and intrinsic vulnerability [ 207 ].

Proteasome blockade induces ER stress, immunoglobulin retention, UPR hyperactivation, and disruption of survival transcriptional programs. Experimental data indicate that the magnitude of UPR activation correlates with PI sensitivity, supporting a transcriptional state-dependent response.

Response to PIs is heterogeneous and reflects tumor-intrinsic programs and inter-individual modifiers. Tumor genomic architecture can increase protein synthesis, secretory burden, and regulatory instability, elevating proteostasis workload and UPS dependence [ 208 , 209 ]. Next-generation inhibitors differ in subunit selectivity and depth of catalytic blockade, modulating stress-network disruption and response patterns [ 208 , 209 ]. Gene expression-based predictors further support a transcriptionally determined component of PI response [ 210 ]. Germline polymorphisms in proteasome-related genes have been reported, although most signals remain candidate modifiers without clinical validation [ 211 ].

Differences between Btz, carfilzomib (Kfz), and ixazomib (Ixa) reflect distinct subunit engagement and depth of catalytic blockade superimposed on shared UPS dependence.

Btz is a reversible β5 subunit inhibitor that perturbs proteostasis and stress-response networks in MM cells. It induces proteotoxic stress, activates the UPR, and modulates NF-κb signaling in a context-dependent manner, targeting genetically constrained survival circuits [ 209 ].

UPR effectors including PERK and ATF4 are rapidly activated. Baseline and inducible expression of UPR regulators such as XBP1, ATF3 , and ATF4 has been associated with differential Btz sensitivity, supporting transcriptional state-dependent response [ 212 , 213 ]. NF-κb effects are context dependent and can include canonical activation, underscoring condition-dependent pathway modulation [ 214 ].

Gene-expression signatures further support drug-class–specific transcriptional determinants of response [ 210 ].

Pharmacogenomic studies suggest that regulatory variation may contribute to response heterogeneity [ 211 ]. Variants in proteasome subunits (e.g., PSMB5, PSMB6, PSMB9) have been associated with differential response in exploratory cohorts, although survival correlations remain inconsistent [ 215 , 211 ].

Resistance emerges through transcriptional and genetic adaptation, including rewiring, UPS dysregulation, and therapy-driven clonal selection [ 216 ]. Refractory disease frequently coexists with broader signaling alterations, including RAS-pathway mutations [ 217 ]. CRISPR-based screens indicate distributed genetic control of PI sensitivity rather than single dominant resistance drivers [ 218 ].

Kfz, through irreversible binding and deeper catalytic blockade, may partially overcome resistance linked to reversible β5 inhibition (Ito, 2020; [ 219 ]). However, resistance ultimately emerges through similar adaptive remodeling mechanisms. Exploratory pharmacogenomic data suggest that germline variation outside proteasome subunits may influence outcomes. However, they remain hypothesis-generating [ 220 ].

Ixa is an oral β5-targeting PI with partially overlapping resistance patterns [ [216] , [221] ]. Preclinical data suggest multiple regulatory determinants of Ixa sensitivity. Wild-type KRAS status has been associated with greater Ixa sensitivity than activating KRAS variants [ 221 ]. In lymphoid malignancies, sensitivity correlates with immunoproteasome activity and can be enhanced by interferon-driven immunoproteasome induction, implicating inducible subunit composition as a regulatory layer [ 222 ]. Additional studies implicate checkpoint kinase- and MYC -associated transcriptional programs in response heterogeneity [ 223 ]. In myeloma-related models, Ixa has been linked to induction of miR-33b with downstream inhibition of PIM1 , and to microenvironmental effects including stronger disruption of NF-κB signaling in stromal compartments than Btz, with reduced cytokine-mediated resistance and effects on osteoclast/osteoblast balance [ 224 ].

Clinically, IRd significantly prolongs PFS in RRMM in a phase III trial, with benefit observed across prespecified subgroups including high-risk cytogenetic categories [ 225 ]. High-risk abnormalities such as del(17p), t(4;14), t(14;16), and 1q21 gain remain associated with inferior PFS overall [ 226 ]. Subgroup analyses suggest that IRd reduces the hazard of progression versus Rd alone in high-risk patients, indicating partial mitigation of cytogenetic risk rather than full risk neutralization. Very high clonal burden of del(17p) remains associated with the poorest outcomes [ 227 ]. Earlier phase I/II studies also supported activity and tolerability [ 226 ].

From a pharmacogenetic perspective, no germline SNPs in proteasome subunits or angiogenesis pathways have been validated as predictors of Ixa efficacy or toxicity. Although preclinical work supports selective subunit inhibition balance, clinical translation remains limited, and biomarker-driven studies are needed to define robust predictors in high-risk and combination-treated populations.

Genetic determinants of response to IMiDs and immunotherapies

IMiDs (thalidomide, lenalidomide, pomalidomide) are central immune-modulating agents in MM, combining tumor-intrinsic transcriptional targeting with immune and microenvironmental effects [ 228 ].

IMiDs bind cereblon (CRBN), the substrate receptor of a cullin-RING E3 ligase complex, inducing degradation of IKZF1 and IKZF3 and defining the CRBN-IKZF-IRF4 axis [ [228] , [229] ]. This leads to IRF4 and MYC downregulation and reprogramming of survival networks, while simultaneously enhancing T- and NK-cell function [ 228 , 230 ]. Alterations affecting CRBN and related pathway components, together with transcriptional rewiring and clonal selection under treatment pressure, are key determinant of resistance [ 231 , 232 ].

Beyond mutations and CNAs, treatment-selected DNA methylation changes also contribute. CRBN flanking/enhancer hypermethylation (up to 64% of IMiD-resistant cases) and PSMD5 promoter hypermethylation (15–24% of PI-refractory cases), both correlating with reduced expression [ 233 ].

Genetic and microenvironmental basis for immune-based therapies in MM

MM is characterized by genetically and transcriptionally driven immune dysregulation and immune escape. Malignant plasma cells reshape the bone marrow niche through cytokine signaling, stromal interactions, and immune checkpoint modulation, generating an immunosuppressive microenvironment that suppresses anti-tumor immune responses [ [215] , [234] , 235 ].

Soluble mediators (IL-6 and TGF-β), adhesion-dependent signaling, and checkpoint pathways (PD-L1 and TIGIT), support this suppressive niche. High-risk lesions, such as del(17p), t(4;14), and 1q gain have been associated with activation of NF-kB/IRF4-centered networks and APRIL/BAFF signaling, potentially favoring expansion of immunosuppressive populations and impair NK and CD8⁺ T-cell cytotoxicity. MYC -rearranged disease has also been linked to reduced antigen-presentation programs, including MHC-I downregulation, further promoting immune evasion.

Microenvironmental circuits involving macrophage polarization, dendritic-cell dysfunction, regulatory T-cell expansion, and impaired NK-cell activity further limit antitumor immunity and contribute to resistance [ 235 ].

These immune alterations provide the biological substrate for immune-based therapies and partly explain synergy between monoclonal antibodies and IMiDs [ 234 ].

IMiDs and cereblon modulators

Genetic and regulatory variation may modulate IMiD response, but is not yet clinically consolidated. Efficacy reflects tumor dependency on CRBN-centered programs plus host immune context, whereas resistance involves CRBN-pathway disruption, transcriptional rewiring, and clonal selection [ 231 , 232 ]. In MM, TLR4 and TIRAP SNPs were associated with lower response rates and shorter PFS/OS in IMiD-containing primary therapy [ 236 ]. SNPs in ADME genes such as ABCB1, ABCG2 and CYP3A4 have been associated with response/toxicity to lenalidomide-based therapy [ 237 ]. Germline variation within the CRBN has also been implicated: minor alleles at rs1714327 and rs1705814 were associated with outcomes in thalidomide-containing regimens (CTD) [ 179 ]. However, reproducible genome-wide significant loci for IMiD-specific response are lacking, and most SNP associations remain suggestive; clinical implementation is premature.

Antibody-based therapies

Efficacy of antibody-based therapies is determined by antigen locus integrity, transcriptional regulation of target density, and host immunogenetic background [ 238 , 239 ]. Anti-CD38 therapy depletes CD38⁺ immune regulatory populations and reshapes T-cell repertoires, linking antigen expression to immune-mediated clearance [ 240 ]. Inherited antigen sequence variation can weaken epitope recognition and contribute to primary resistance, supported by variant-level analyses showing loss of antibody binding due to germline antigen variation [ 241 ]. Antigen-density heterogeneity and host immunogenetic variation, including Fcγ receptor polymorphisms, further modulate response [ 242 ].

T-cell-redirecting strategies

T-cell-redirecting therapies are tightly coupled to antigen-locus integrity, clonal architecture, and immune transcriptional states. Bispecific antibodies and CAR-T cells bypass MHC restriction but impose strong selective pressure that favors antigen-loss variants and immune-state shifts [ 243 ].

Bispecifics and CAR-T constructs engage CD3 and redirect cytotoxicity toward antigens such as BCMA and GPRC5D [ 244 ]. Activity depends on antigen density/epitope architecture and T-cell activation states, while pre-existing heterogeneity provides a substrate for escape [ 243 ] ( Table 4 ). Table 4 Genetic and transcriptional escape mechanisms in T-cell–redirecting therapies. Table 4 dummy alt text TARGET THERAPY TYPE GENETIC / MOLECULAR ALTERATION REPORTED PATTERN FUNCTIONAL IMPLICATION REFERENCE BCMA (TNFRSF17) BCMA CAR-T Biallelic loss (deletions) Rare but confirmed in post-CAR-T relapse Loss of BCMA expression, absent soluble BCMA, impaired CAR-T expansion [ 245 ] BCMA (TNFRSF17) CAR-T / bispecific antibodies Biallelic deletions and mutations Subset of post-BCMA immunotherapy relapses Target loss–mediated escape [ 246 ] BCMA (TNFRSF17) CAR-T / bispecific antibodies Co-alterations in NF-κB regulators (CYLD, TRAF3) Enriched in resistant/high-risk cases Compensatory survival signaling [ [243] , [246] ] GPRC5D Bispecific antibodies / CAR-T Biallelic deletions, copy-number loss, regulatory loss Recurrent mechanism at relapse after GPRC5D-targeted therapy Reduced or absent antigen density [ 246 ] — BCMA CAR-T Transcriptional reprogramming (SOX4, CD24 ↑; SDC1, XBP1 ↓) Residual post-therapy clones Dedifferentiation and lineage plasticity [ [245] , [246] ] — CAR-T / bispecific antibodies T-cell exhaustion signatures (PDCD1, HAVCR2, LAG3 ↑) Resistance with retained antigen Effector T-cell dysfunction [ 243 ] Genetic and transcriptional mechanisms associated with resistance to T-cell–redirecting therapies in MM include target-antigen loss through biallelic deletions, mutations, and CNVs, as well as non-genetic escape programs such as tumor-cell dedifferentiation and T-cell exhaustion. Reported frequencies vary across studies and platforms and are often derived from small, genomically profiled relapse cohorts using bulk and/or single-cell sequencing approaches. Alterations in NF-κB pathway regulators (e.g., CYLD, TRAF3) are shown when co-occurring with antigen loss or resistance phenotypes.

Biallelic loss of TNFRSF17 (BCMA) is a demonstrated resistance mechanism to BCMA CAR-T, with absent soluble BCMA and impaired CAR-T expansion [ 245 ]. BCMA loss also occurs after BCMA bispecifics, indicating a shared escape route across platforms; although uncommon (<5% of relapses), acquired biallelic TNFRSF17 loss is a convergent treatment-selected mechanism [ [246] , [247] , [248] , [249] ]. GPRC5D loss is recurrent after GPRC5D -directed therapies [ 246 ], and high-risk features such as 1q gain and NF-κb lesions (e.g., CYLD, TRAF3 ) associate with inferior PFS [ [243] , [246] ].

Therapeutic pressure plays a central role in shaping clonal evolution, with different treatment classes imposing distinct selective bottlenecks, consistent with immunoediting processes [ 250 ]. Conventional agents such as PIs and IMiDs exert broader cytotoxic and microenvironmental effects, contributing to gradual shifts in clonal composition and promoting the outgrowth of resistant subclones. In contrast, modern immunotherapies, including CAR T-cell therapies and bispecific antibodies, impose highly specific antigen-directed selective pressures. A key mechanism of resistance in this context is antigen escape, characterized by downregulation or loss of target antigens such as BCMA, as well as the expansion of antigen-negative tumor populations [ 251 , 252 ]. Emerging evidence in MM supports both the selection of pre-existing antigen-negative subclones and the acquisition of genetic alterations affecting target antigens under immune pressure. These mechanisms enable tumor cells to evade immune-mediated killing and represent a major driver of relapse following immunotherapy. However, while antigen escape is increasingly well characterized in the context of immunotherapy, direct comparative evidence defining how distinct therapeutic classes differentially shape clonal bottlenecks remains limited. Understanding antigen escape and therapy-specific evolutionary bottlenecks will be critical for the development of next-generation strategies aimed at preventing resistance and achieving durable responses.

Post-BCMA CAR-T relapse can show stemness programs (e.g., SOX4, CD24 ) with reduced plasma-cell markers (e.g., SDC1, XBP1 ), consistent with plasticity [ [245] , [246] ]. Dual-targeted BCMA/CD24 CAR-T improves control in models (Sun et al., 2024). These observations motivate combinations aimed at restoring T-cell fitness to improve durability [ 253 , 254 ].

Biomarkers and resistance under immune pressure

Tumor-intrinsic genomic architecture influences response depth and durability, and high-risk lesions such as del(17p), 1q gain, and alterations in NF-κB regulators including CYLD and TRAF3 have been associated with inferior outcomes after T-cell-redirecting therapies [ [243] , [246] ]. Integrity of the therapeutic target locus is a direct determinant of susceptibility, as biallelic loss or focal deletion of TNFRSF17 or GPRC5D can mediate resistance to BCMA- and GPRC5D -directed platforms [ [245] , [246] ]. More broadly, increased genomic complexity and subclonal diversity contribute to response heterogeneity, consistent with models linking intratumoral diversity to therapeutic resistance [ 255 ].

Transcriptomic and immunoregulatory programs provide complementary biomarkers. Post–BCMA CAR-T relapse samples may show enrichment of stemness-associated programs (e.g., SOX4, CD24) together with downregulation of plasma-cell differentiation markers (e.g., SDC1, XBP1), consistent with plasticity and dedifferentiation as escape routes [ [245] , [246] ]. In parallel, expanded T-cell populations at relapse frequently display exhaustion-associated signatures with increased expression of inhibitory receptor genes including PDCD1, HAVCR2 , and LAG3 , linking immune-state profiles with functional resistance and limited persistence of redirected T-cell responses [ 243 ]. Host immunogenetic background and the bone marrow microenvironment further modulate efficacy. Germline variation in immune signaling and antigen-presentation pathways, including TLR-axis and HLA polymorphisms, has been associated with differential response and survival in IMiD-containing regimens [ 236 , 256 ]. At the microenvironmental level, immunosuppressive myeloid populations, regulatory T cells, and dysfunctional antigen-presenting cells can reduce effector activity and limit redirected T-cell effectiveness.

This structural and functional complexity limits the predictive value of single-analyte biomarkers and supports composite models incorporating immune-context variables [ 257 ]. Longitudinal analyses indicate that immune therapies constrain clonal diversity and select resistant populations, consistent with immunoediting [ 258 ]. Overall, available evidence supports composite biomarkers integrating antigen density, tumor genomics, transcriptional states, and microenvironmental features to improve response prediction and inform stratified sequencing and combination strategies.

Genetically stratified targeted therapies

Genetically stratified targeted therapies in MM exploit tumor-specific genetic and epigenetic vulnerabilities identified through cytogenetic and regulatory profiling. Agents such as HDAC and BCL-2 inhibitors exemplify mechanism-based strategies directed at epigenetic dysregulation and apoptotic dependency, illustrating the shift toward molecular selection based on tumor architecture ( Table 5 ). Table 5 Examples of genetically stratified targeted strategies in MM. Table 5 dummy alt text GENETIC FEATURE TARGETED STRATEGY / AGENT BIOLOGICAL RATIONALE KEY REFERENCES T(11;14) Venetoclax BCL-2 dependency and mitochondrial priming profile [ 259 , 260 ] T(4;14) / MMSET HDAC inhibitor–based combinations Epigenetic dysregulation and chromatin-regulatory dependency [ 261 , 262 ] DEL(17P), 1Q GAIN Intensified / targeted combinations Relative MCL-1 or BCL-xL dependence and network instability [ [261] , [263] ] Illustrative examples of genetically stratified targeted approaches in MM. Strategies reflect enrichment of therapeutic sensitivity in biologically defined subgroups and should be interpreted within combination-regimen and clinical-trial contexts.

HDAC inhibitors: epigenetic vulnerability and regulatory network targeting

Epigenetic dysregulation is a recurrent feature of MM and provides a rationale for targeting chromatin and transcriptional control. HDAC inhibitors disrupt gene-expression programs and appear particularly relevant in biologically defined subsets such as t(4;14) disease with MMSET overexpression, where epigenetic dependency may be accentuated. Their synergy with proteasome inhibitors reflects convergence of proteostasis stress and epigenetic deregulation, supporting use in rational combinations rather than as standalone agents [ 261 , 262 ].

BCL-2 inhibitors: genetically defined apoptotic dependency

Targeting apoptotic circuitry is one of the clearest examples of stratified therapy in MM. Venetoclax exploits apoptotic vulnerability most strongly in t(11;14) disease, which typically displays transcriptional and mitochondrial profiles consistent with BCL-2 dependence, partly linked to CCND1 dysregulation. Mechanistically, venetoclax binds BCL-2, displaces pro-apoptotic factors such as BIM, activates BAX/BAK, and induces mitochondrial outer membrane permeabilization and selective apoptosis [ 264 ].

Sensitivity correlates with high BCL2 / BCL2L1 mRNA ratios and mitochondrial priming, whereas dependency on MCL-1 or BCL-xL is more typical outside t(11;14), including in high-risk subgroups. Clinical studies confirm higher response rates in relapsed t(11;14)-positive patients, particularly in combination regimens [ 259 , 260 ].

Stratification using FISH together with expression-based dependency markers can refine selection, while activity outside molecularly selected subgroups is reduced and may be attenuated by lesions such as 1q gain. Emerging extensions include MCL-1 inhibitors and broader BH3-mimetic strategies targeting non-BCL-2-dependent clones.

Other targeted agents and antigen-defined strategies

Beyond epigenetic and apoptotic targeting, additional precision strategies address surface antigens and deregulated signaling networks within a context of antigen heterogeneity and clonal selection. High-risk features such as 1q gain and del(17p), which disrupt cell-cycle and p53-centered programs, increasingly justify intensified regimens and investigational combinations including nuclear export (XPO1) inhibition. Whole-genome features such as chromothripsis and APOBEC mutational activity further refine biological risk and support escalation beyond standard backbones, consistent with network-level, genetically informed combination strategies [ 261 , 262 ].

Germline genetics and clinical outcomes in MM

Disease progression and survival

Interindividual differences in progression and survival in MM are not fully explained by tumor burden, cytogenetics, or treatment response alone [ 265 ]. Real-world analyses show that comorbidity burden influences early mortality, particularly within the first year after diagnosis [ 266 ], yet host germline variation also contributes to heterogeneity in progression dynamics and outcomes [ 267 ]. Inherited factors may modulate plasma-cell biology, immune interactions, and treatment tolerance, shaping both precursor evolution and durability of response [ 88 ].

GWAS have identified survival-associated loci, including variants at 16p13 linked to overall survival independently of conventional risk markers [ 78 ]. A subset of patients carries pathogenic inherited variants in DNA damage–repair genes such as BRCA1, BRCA2, CHEK2 , and ATM , enriched relative to the general population [ 130 , 265 ]. Common germline loci also influence immune regulation and plasma-cell function [ 75 ]. Notably, the clinical impact of DNA repair-deficient variants appears therapy dependent, as carriers may experience comparable or even improved outcomes with intensive DNA-damaging approaches, including high-dose melphalan and autologous transplantation [ 268 , 269 ]. In addition, non-coding variants affecting transcriptional programs, including IRF4 -related pathways, have been associated with survival differences [ 270 ].

Treatment-Related toxicity

Treatment-related toxicity partly reflects inherited differences in drug-metabolizing enzymes, transporters, DNA repair pathways, and stress-response genes [ 271 ].

These germline factors interact with tumor biology, the microenvironment, and treatment characteristics to influence tolerance and complications such as neuropathy, cytopenias, infections, thromboembolism, renal injury, and second malignancies [ [272] , [273] , [274] ]. Toxicities from IMiDs and alkylators further suggest interactions between DNA-damaging exposure, marrow reserve, and DNA-repair capacity, although validated germline predictors remain limited [ 229 , 230 ].

Peripheral neuropathy

Peripheral neuropathy, particularly with Btz and thalidomide, is influenced by host germline variation [ 275 ]. Btz-induced neuropathy involves axonal injury, mitochondrial dysfunction, and stress-response dysregulation, with susceptibility linked to variants at the PKNOX1/CBS locus and to inflammatory or angiogenic profiles such as IL-8 and VEGF [ [276] , [277] ]. Thalidomide-related neuropathy also shows interindividual variability consistent with genetic differences in neuronal resilience and repair [ 278 ].

Bone disease

Myeloma-associated bone disease reflects disrupted bone remodeling driven by malignant plasma cells and the microenvironment, leading to osteolytic lesions and skeletal-related events [ [279] , [280] , [281] , [282] ]. Skeletal complications and altered bone formation markers may appear even in smoldering stages [ 283 ].

Interindividual variability in severity suggests a contribution of host germline variation. Variants in pathways regulating bone remodeling, immune signaling, and Wnt activity may influence susceptibility to osteolytic damage and skeletal involvement at diagnosis [ 284 ]. These data support integrating germline profiling into skeletal-risk models, particularly in MGUS or smoldering disease where bone changes may precede systemic progression [ 285 ].

Gastrointestinal toxicity and mucositis

Susceptibility to gastrointestinal toxicity and mucositis is partly influenced by inherited variation [ 286 ]. These complications can impair quality of life and treatment adherence [ 287 ]. Variation in genes regulating drug metabolism, mucosal repair, inflammatory signaling, and epithelial integrity likely modulates severity, and GWAS in transplant recipients have identified loci associated with chemotherapy-induced oral mucositis [ 288 ].

Thromboembolic complications

Inherited germline variation contributes to thromboembolic risk in MM, which arises from both disease- and treatment-related prothrombotic states [ 289 ]. Variants affecting coagulation and endothelial pathways, including F5, F2, MTHFR , and SERPINE1 , may predispose subsets of patients to venous thromboembolism [ 290 , 291 ]. These genetic differences interact with inflammation and treatment exposure to shape individual susceptibility.

Clinical implications and future directions

Future research in MM should prioritize integrated longitudinal designs that link genomic architecture to functional and clinical phenotypes. Although current cytogenetic and sequencing frameworks define major driver categories, important gaps remain in subclonal reconstruction, evolutionary timing, and functional interpretation. This limitation is particularly relevant in the context of MM as a genetically complex and evolutionarily dynamic disease, characterized by marked clonal heterogeneity and a continuous transition from precursor states to symptomatic disease [ 292 ]. Serial multi-omics profiling combining WGS, transcriptomics, epigenomics, and single-cell approaches will be essential to map clonal dynamics under treatment pressure and distinguish early driver events from adaptive changes. In this context, single-cell profiling technologies represent a key opportunity to resolve intratumoral heterogeneity and refine precision oncology strategies [ 152 ].

From a clinical perspective, the translation of these genomic insights into routine practice remains incomplete. Current risk stratification systems, including the Revised International Staging System (R-ISS), incorporate selected cytogenetic abnormalities such as del(17p), t(4;14), and 1q gain, but largely rely on static genomic features and do not capture clonal heterogeneity, subclonal dynamics, or treatment-driven evolution. Integrating longitudinal genomic data, MRD assessment, and clonal architecture into these frameworks represents a key opportunity to refine prognostic accuracy and enable adaptive risk stratification.

Improving genomic resolution in routine practice is equally important. Broader implementation of high-coverage sequencing, optical mapping, and chromatin-conformation–aware methods may reveal structural variants and regulatory disruptions not captured by standard FISH or targeted panels. Harmonized testing platforms and reporting standards will be required to ensure reproducible and clinically actionable stratification. However, the implementation of advanced genomic approaches, including subclonal inference from bulk sequencing data, remains limited by the lack of standardized bioinformatic pipelines, methodological variability, and restricted accessibility in clinical laboratories.

From the germline perspective, systematic predisposition-testing approaches are needed, as current criteria likely underestimate inherited risk. Integration of germline sequencing, polygenic risk scores, and family-based screening approaches may enable earlier identification of high-risk individuals and inform surveillance strategies in MGUS and SMM, including assessment of gene–environment interactions. Nevertheless, routine germline testing is not yet widely implemented outside selected clinical contexts, as penetrance, clinical utility, and management guidelines remain insufficiently defined. In addition, PRS, despite demonstrating population-level risk stratification, are not currently clinically actionable due to the lack of prospective validation, reduced performance across diverse ancestries, and absence of standardized thresholds or integration frameworks with established clinical risk factors.

Functional validation remains critical, as many GWAS loci and CNVs map to regulatory regions with incompletely defined roles. CRISPR-based perturbation screens and microenvironment-aware models can translate association signals into pathway dependencies and therapeutic targets, guiding rational combination strategies and resistance-prevention approaches. Unresolved questions remain regarding the interaction between germline susceptibility, somatic evolution, and environmental exposures, highlighting the need for integrative studies that connect genetic architecture with functional and clinical phenotypes.

Clinical translation will require embedding genomic data into adaptive trial designs supported by real-time molecular monitoring. Biomarker-driven and genomically stratified trials, complemented by multi-layer data modeling and artificial intelligence approaches, can enable evolution-aware precision management rather than static risk assignment. From a therapeutic standpoint, genomic information is increasingly relevant for treatment selection and monitoring, including the identification of targetable dependencies (e.g., BCL-2 in t(11;14) disease) and the anticipation of resistance mechanisms such as antigen escape in T-cell–redirecting therapies. Integration of genomic, transcriptomic, and immune profiling into treatment algorithms may enable more precise patient selection, inform combination strategies, and improve long-term disease control. Ultimately, bridging the gap between genomic discovery and clinical implementation will be essential to fully realize the potential of precision oncology in MM.

Conclusions

MM is defined by a multilayered genomic architecture in which primary cytogenetic events, secondary structural alterations, copy-number remodeling, somatic mutations, and inherited susceptibility collectively shape disease behavior. IgH translocations and hyperdiploidy establish early biological subtypes, whereas arm-level and focal CNVs, MYC activation, and TP53 loss drive progression, heterogeneity, and therapy-influenced clonal evolution.

Germline predisposition and polygenic susceptibility further modulate interindividual risk and interact with environmental and immune factors, particularly in precursor states, reinforcing the concept of MM as a genomically defined disease spectrum rather than a purely clinicopathologic entity.

Despite these advances, translation into uniformly effective precision care remains incomplete, as detection gaps, subclonal complexity, and limited functional interpretation constrain predictive accuracy. An integrated precision framework linking cytogenetics, comprehensive sequencing, germline risk, immune context, and dynamic clonal assessment provides a realistic path to improve early detection, personalize therapy, delay resistance, and enhance long-term outcomes.

However, several key barriers continue to limit clinical implementation. Emerging tools such as PRSs and subclonal reconstruction methodologies, while conceptually aligned with this framework, are not yet standardized or clinically actionable. Overcoming these limitations will require prospective validation, harmonized analytical pipelines, and integration of genomic data into real-time clinical decision-making. Addressing these challenges will be essential to fully translate the evolutionary genomics approach into effective precision oncology strategies in MM.

CRITICAL VIEW

Most reviews in multiple myeloma (MM) genetics still treat germline risk, primary cytogenetics, and relapse biology as separable layers, often summarized as static “lists” of lesions or biomarkers. This review takes a different stance: MM is best conceptualized as a dynamic evolutionary genomic ecosystem, where germline susceptibility and somatic architecture are not parallel narratives but interacting determinants of clonal initiation, diversification, and therapy-driven selection. Our central contribution is therefore an evolution-aware integrative framework that links (i) inherited predisposition (polygenic burden and rare predisposition variants), (ii) founding genomic events (IgH translocations and hyperdiploidy), (iii) progression-associated remodeling (CNV restructuring, MYC dysregulation, TP53 loss, complex structural variation), and (iv) treatment adaptation (immune pressure, antigen escape, and transcriptional plasticity) into a single coherent model of disease behavior.

How this review distinguishes itself from existing literature. We move beyond “high-risk feature” cataloguing by emphasizing trajectory rather than status: the clinically relevant question is increasingly which evolutionary path a patient’s disease is on, not merely which lesion is present at diagnosis. In practice, this shifts attention toward clonal architecture, timing of events, and the selective pressures imposed by modern therapy. We highlight that the same genomic feature can carry different implications depending on context (co-lesions, subclonal fraction, prior therapies, immune landscape), and we explicitly frame resistance as the predictable outcome of repeated evolutionary bottlenecks. This perspective naturally connects cytogenetics and sequencing outputs to actionable clinical hypotheses: anticipating relapse routes, prioritizing early intensification for unstable evolutionary states, and designing combinations that reduce the probability of convergent escape.

Scope and depth of coverage. The review provides deep coverage of: (1) inherited predisposition (familial clustering, rare pathogenic germline variants, GWAS/PRS and functional interpretation), (2) primary cytogenetic subtypes and how they constrain downstream evolution, (3) CNV and structural-variant remodeling as major engines of progression and relapse, (4) modern genomic detection strategies (from FISH and targeted panels to WGS/WES and optical genome mapping), and (5) therapy-driven adaptation, with emphasis on immunotherapy-era resistance (including antigen loss and immune-state failure). We provide selective, mechanism-focused coverage of the microenvironment and epigenetics only insofar as they modulate genomic instability, immune escape, or treatment selection; an exhaustive review of microenvironmental biology, MRD methodology, or non-genetic pharmacology is intentionally outside our remit.

Why this matters now. MM treatment has entered an era in which deep responses are achievable, yet durable control is still limited by evolutionary inevitability under sequential selective pressures. A static genomic snapshot is no longer enough. We argue that the next practical advance will come from evolution-aware precision myeloma, integrating germline and tumor genomics with longitudinal monitoring to (i) identify patients at risk before symptomatic disease, (ii) detect early divergence toward high-risk trajectories, and (iii) pre-empt convergent resistance pathways—particularly under immune-based therapies where antigen escape and immune dysfunction can rapidly dominate outcomes. In short, our review aims to reposition MM genomics from descriptive stratification to predictive evolutionary management, providing a conceptual and translational bridge between inherited risk, clonal dynamics, and treatment durability.

CRediT authorship contribution statement

María Carretero-Fernández: Writing – review & editing, Writing – original draft, Methodology, Investigation. Antonio José Cabrera-Serrano: Writing – review & editing, Writing – original draft, Methodology, Investigation. Lucía Ruíz Durán: Writing – review & editing, Writing – original draft, Methodology, Investigation. Mariam Ibañez: Writing – review & editing, Investigation. Marco Bonilla: Writing – review & editing, Writing – original draft, Methodology, Investigation. Francisco Mesa: Writing – review & editing, Methodology, Investigation. Juan Francisco Gutiérrez-Bautista: Writing – review & editing, Methodology, Investigation. Rachid Chahboun: Writing – review & editing, Methodology, Investigation. Fernando Jesús Reyes-Zurita: Writing – review & editing, Methodology, Investigation. Joaquín Martínez-Lopez: Writing – review & editing, Methodology, Investigation. Rosa Collado: Writing – review & editing, Methodology, Investigation. Juan Sainz: Writing – review & editing, Writing – original draft, Methodology, Investigation, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare no conflict of interest. The funders had no role in the writing of the manuscript, or in the decision to publish the reviewed material.

References

1 Kumar S.K. Rajkumar V. Kyle R.A. Van Duin M. Sonneveld P. Mateos M.V. Gay F. Anderson K.C. Multiple myeloma Nat. Rev. Dis. Primers. 3 2017 10.1038/NRDP.2017.46;SUBJMETA 17046- 28726797

2 Modi S. Syed S. Shandilya A. Durga D. Yadav R.P. Lakkimsetti M. Amin V. Patel J. Jain H. Desai H.D. Markandu K. Dharia A.A. Evolving burden of multiple myeloma and its trend in European Union countries from 1990 to 2021: a secondary analysis from the Global Burden of Disease Study 2021 Blood 144 2024 6998 10.1182/blood-2024-211305

3 Turesson I. Bjorkholm M. Blimark C.H. Kristinsson S. Velez R. Landgren O. Rapidly changing myeloma epidemiology in the general population: increased incidence, older patients, and longer survival Eur. J. Haematol. 101 2018 237 244 10.1111/EJH.13083;WGROUP:STRING:PUBLICATION PMC6195866 29676004

4 Rajkumar S.V. Multiple myeloma: 2018 update on diagnosis, risk-stratification, and management Am. J. Hematol. 93 2018 1091 1110 10.1002/AJH.25117;PAGEGROUP:STRING:PUBLICATION PMC6223128 30400719

5 Landgren O. Kyle R.A. Pfeiffer R.M. Katzmann J.A. Caporaso N.E. Hayes R.B. Dispenzieri A. Kumar S. Clark R.J. Baris D. Hoover R. Rajkumar S.V. Monoclonal gammopathy of undetermined significance (MGUS) consistently precedes multiple myeloma: a prospective study Blood 113 2009 5412 5417 10.1182/BLOOD-2008-12-194241 19179464 PMC2689042

6 Ríos-Tamayo R. Sánchez M.J. Puerta J.M. Sáinz J. Chang D.Y.L. Rodríguez T. López P. de Pablos J.M. Navarro P. de Veas J.L.G. Romero A. Garrido P. Moratalla L. Alarcón-Payer C. López-Fernández E. González P.A. Jiménez-Moleón J.J. Calleja-Hernández M.Á. Jurado M. Trends in survival of multiple myeloma: a thirty-year population-based study in a single institution Cancer Epidemiol. 39 2015 693 699 10.1016/j.canep.2015.08.002 26277330

7 Davis L.N. Sherbenou D.W. Emerging therapeutic strategies to overcome drug resistance in multiple myeloma Cancers 13 2021 1686 10.3390/CANCERS13071686 2021VolPage131686 33918370 PMC8038312

8 Solimando A.G. Malerba E. Leone P. Prete M. Terragna C. Cavo M. Racanelli V. Drug resistance in multiple myeloma: soldiers and weapons in the bone marrow niche Front. Oncol. 12 2022 10.3389/FONC.2022.973836 PMC9533079 36212502

9 Ramberger E. Sapozhnikova V. Ng Y.L.D. Dolnik A. Ziehm M. Popp O. Sträng E. Kull M. Grünschläger F. Krüger J. Benary M. Müller S. Gao X. Murgai A. Haji M. Schmidt A. Lutz R. Nogai A. Braune J. Laue D. Langer C. Khandanpour C. Bassermann F. Döhner H. Engelhardt M. Straka C. Hundemer M. Beule D. Haas S. Keller U. Einsele H. Bullinger L. Knop S. Mertins P. Krönke J. The proteogenomic landscape of multiple myeloma reveals insights into disease biology and therapeutic opportunities Nat. Cancer 5 8 2024 1267 1284 10.1038/s43018-024-00784-3 20245 38942927 PMC11358022

10 Avet-Loiseau H. Davies F.E. Samur M.K. Corre J. D’Agostino M. Kaiser M.F. Raab M.S. Weinhold N. Gutierrez N.C. Paiva B. Neri P. Weisel K. Maura F. Walker B.A. Bustoros M. Keith Stewart A. Usmani S.Z. Hillengass J. Joo Chng W. Keats J.J. Martinez-Lopez J. Sperling A.S. Touzeau C. Zhan F. Raje N.S. Cavo M. Bolli N. Ghobrial I.M. Dhodapkar M.V. Jagannath S. Spencer A. Parekh S. Bahlis N.J. Lonial S. Sonneveld P. Bergsagel L. Orlowski R.Z. Morgan G. Mateos M.V. Rajkumar S.V. San Miguel J.F. Anderson K.C. Moreau P. Kumar S. Prósper F. Munshi N.C. International Myeloma Society/International Myeloma Working Group Consensus recommendations on the definition of high-risk multiple Myeloma J. Clin. Oncol. 43 2025 2739 2751 10.1200/JCO-24-01893 40489728 PMC13445576

11 Butler C. Wolff D.J. Kang Y. Stuart R.K. Costa L.J. Association of age with fluorescence in situ hybridization abnormalities in multiple myeloma reveals higher rate of IGH translocations among older patients Leuk. Lymphoma 53 2012 2444 2448 10.3109/10428194.2012.691483 22574971

12 Williams L. Boyle E.M. Davies F.E. Walker B.A. Ashby C. Bauer M.A. Wardell C.P. Flynt E. Thakurta A. Morgan G. The spectrum of exomic mutation in elderly myeloma differs substantially from patients at younger ages consistent with a different evolutionary trajectory to full blown disease based on age of onset Blood 134 2019 4346 10.1182/BLOOD-2019-130079 –4346

13 Ghosh A.K. McGuire N. Ruiz Vega R. Alaws H. Adewunmi C. Abidoye O. Caton A.P. Johnson A.T. Identifying associations between race and gender in the incidence and mortality of patients with multiple myeloma J. Clin. Oncol. 41 2023 e20052–e20052 10.1200/jco.2023.41.16_suppl.e20052

14 Mafra A. Laversanne M. Marcos-Gragera R. Chaves H.V.S. Mcshane C. Bray F. Znaor A. The global multiple myeloma incidence and mortality burden in 2022 and predictions for 2045 JNCI 117 2025 907 914 10.1093/JNCI/DJAE321 39658225

15 Bird S. Cairns D. Menzies T. Boyd K. Davies F. Cook G. Drayson M. Gregory W. Jenner M. Jones J. Kaiser M. Owen R. Jackson G. Morgan G. Pawlyn C. Sex differences in multiple myeloma biology but not clinical outcomes: results from 3894 patients in the Myeloma XI trial Clin. Lymphoma Myeloma Leuk. 21 2021 667 675 10.1016/j.clml.2021.04.013 34059488 PMC8528179

16 Waxman A.J. Mink P.J. Devesa S.S. Anderson W.F. Weiss B.M. Kristinsson S.Y. McGlynn K.A. Landgren O. Racial disparities in incidence and outcome in multiple myeloma: a population-based study Blood 116 2010 5501 5506 10.1182/BLOOD-2010-07-298760 20823456 PMC3031400

17 Greenberg A.J. Vachon C.M. Rajkumar S.V. Disparities in the prevalence, pathogenesis and progression of monoclonal gammopathy of undetermined significance and multiple myeloma between blacks and whites Leukemia 26 4 2011 609 614 10.1038/leu.2011.368 201226 22193966 PMC3629947

18 Huber J.H. Ji M. Shih Y.H. Wang M. Colditz G. Chang S.H. Disentangling age, gender, and racial/ethnic disparities in multiple myeloma burden: a modeling study Nat. Commun. 14 1 2023 10.1038/s41467-023-41223-8 2023145768- PMC10511740 37730703

19 Chen X. Luo T. Zhang W. Wang S. Zhu M. He H. Liu J. Lu J. Qiang W. Jia Y. Hou N. Zhao X. Zhang S. Li J. Du J. Genomic characteristics and prognostic correlations in Chinese multiple myeloma patients BMC Med. Genom. 18 2025 10.1186/s12920-025-02116-5 PMC11907858 40087669

20 Du C. Li L. Fan H. Mao X. Liu J. Xu Y. Sui W. Deng S. Li C. Zhao J. Yi S. Hao M. Zou D. Zhao Y. Qiu L. An G. The age-dependent changes in risk weights of the prognostic factors for multiple myeloma Hematology 2023 28 10.1080/16078454.2023.2258686 37724573

21 Du C. Wang Y. Mao X.-H. Yan Y. Liu J. Fan H. Xu Y. Sui W. Deng S. Mingwei F. Li C. Zhao J. Yi S. Zou D. Zhao Y. Qiu L. An G. The relative risk of prognostic factors of age stratified multiple myeloma Blood 136 2020 26 27 10.1182/blood-2020-140718

22 De Soysa 13, L. Ramasamy K. Knight E. Byun S. Heartin E. Garvey V. Basu S. Stones J. Oakes R. Killingsworth G. Nicholls P. Mahmood S. Bowcock S. Ryan R. Essex S. Alfred A. Collier D. Bradley R. Thomas-Dewing R. Cawley K. Kaczmarski R. Al-Obaidi M. Cubos F. Bomken C. Stern S. Elonen T. Raymode P. Chathurani D. Jenkins L. Bygrave C. Frcpath M.M. Consuegra R.G. Harrison W. Willis F. Brouwer R. Koshiaris C. Vallance G. Gamble V. Javaid K. Sadler R. Pratt G. Secure: a prospective long-term observational study in patients with monoclonal gammopathy of undetermined significance Blood 144 2024 6889 10.1182/BLOOD-2024-203107 –6889

23 Landgren O. Hofmann J.N. McShane C.M. Santo L. Hultcrantz M. Korde N. Mailankody S. Kazandjian D. Murata K. Thoren K. Ramanathan L. Dogan A. Rustad E. Lu S.X. Akhlaghi T. Kristinsson S.Y. Björkholm M. Devlin S. Purdue M.P. Pfeiffer R.M. Turesson I. Association of immune marker changes with progression of monoclonal gammopathy of undetermined significance to multiple myeloma JAMa Oncol. 5 2019 1293 1301 10.1001/JAMAONCOL.2019.1568 31318385 PMC6646992

24 Sra M.S. Kumar S. Risk stratification and early intervention in smoldering multiple myeloma Leuk. Lymphoma 67 2026 55 63 10.1080/10428194.2025.2576562;SUBPAGE:STRING:FULL 41133522

25 Georgakopoulou R. Fiste O. Sergentanis T.N. Andrikopoulou A. Zagouri F. Gavriatopoulou M. Psaltopoulou T. Kastritis E. Terpos E. Dimopoulos M.A. Occupational exposure and Multiple myeloma risk: an updated review of meta-analyses J. Clin. Med. 10 2021 10.3390/JCM10184179 PMC8469366 34575290

26 Khuder S.A. Mutgi A.B. Meta-analyses of Multiple myeloma and farming J. Ind. Med 32 1997 510 516 10.1002/(SICI)1097-0274(199711)32:5 9327075

27 Sergentanis T.N. Zagouri F. Tsilimidos G. Tsagianni A. Tseliou M. Dimopoulos M.A. Psaltopoulou T. Risk factors for Multiple myeloma: a systematic review of meta-analyses Clin. Lymphoma Myeloma Leuk. 15 2015 563 577 10.1016/j.clml.2015.06.003 e3 26294217

28 Lee D.J. El-Khour H. Tramontano A.C. Alberge J.B. Perry J. Davis M.I. Horowitz E. Redd R. Sakrikar D. Barnidge D. Perkins M.C. Harding S. Mucci L. Rebbeck T.R. Ghobrial I.M. Marinac C.R. Mass spectrometry-detected MGUS is associated with obesity and other novel modifiable risk factors in a high-risk population Blood Adv. 8 2024 1737 1746 10.1182/BLOODADVANCES.2023010843 38212245 PMC10997907

29 Marinac C.R. Birmann B.M. Lee I.M. Rosner B.A. Townsend M.K. Giovannucci E. Rebbeck T.R. Buring J.E. Colditz G.A. Body mass index throughout adulthood, physical activity, and risk of multiple myeloma: a prospective analysis in three large cohorts Br. J. Cancer 118 7 2018 1013 1019 10.1038/s41416-018-0010-4 2018118 29527008 PMC5931105

30 Tentolouris A. Ntanasis-Stathopoulos I. Terpos E. Obesity and multiple myeloma: emerging mechanisms and perspectives Semin. Cancer Biol. 92 2023 45 60 10.1016/J.SEMCANCER.2023.04.003 37030643

31 Psaltopoulou T. Sergentanis T.N. Kanellias N. Kanavidis P. Terpos E. Dimopoulos M.A. Tobacco smoking and risk of multiple myeloma: a meta-analysis of 40 observational studies Int. J. Cancer 132 2013 2413 2431 10.1002/IJC.27898 23047159

32 Giovannucci E. Ave H. Chan H.T. An integrative approach for deciphering the causal associations of physical activity and cancer risk: the role of adiposity JNCI 110 2018 935 941 10.1093/JNCI/DJY091 29860428

33 Joseph J.M. Hillengass M. Cannioto R. Tario J.D. Wallace P.K. Attwood K. Groman A. Jacobson H. Wittmeyer B. Mohammadpour H. Abrams S.I. Moysich K.B. Hillengass J. T cell exhaustion markers in multiple myeloma patients are lower after physical activity intervention Clin. Lymphoma Myeloma Leuk. 24 2024 621 628 10.1016/j.clml.2024.04.006 38762420

34 Castro, F., Parikh, R., Eustaquio, J.C., Derkach, A., Joseph, J.M., Lesokhin, A.M., Usmani, S.Z., Shah, U.A., 2023. Pre-diagnosis dietary patterns and risk of multiple myeloma in the NIH-AARP diet and Health study. medRxiv 2023.09.20.23295639. 10.1101/2023.09.20.23295639 . PMC10919351 38158443

35 Isoda A. Miyazawa Y. Ishikawa T. Kanaya S. Nakayama K. Mihara M. Iriuchishima H. Saito A. Matsumoto M. Sawamura M. Prevalence and clinical outcomes of vitamin D deficiency among Japanese multiple myeloma patients: a single-center observational study Supportive Care in Cancer 31 9 2023 10.1007/S00520-023-08021-W 202331547- 37656213

36 Li Y. Li Y. Zhang L. Li W. Hepatitis C virus infection and risk of multiple myeloma: evidence from a meta-analysis based on 17 case-control studies J. Viral. Hepat. 24 2017 1151 1159 10.1111/jvh.12742 28656736

37 Zamani K. Rostami P. Darehbagh R.R. Afraie M. Moradi Y. Hepatitis B and C virus infection and risk of multiple myeloma: a systematic review and meta-analysis BMC. Cancer 25 1 2025 998 10.1186/s12885-025-14420-5 202525- 40468263 PMC12135261

38 Rodríguez-García A. Ancos-Pintado R. García-Vicente R. Ortiz-Ruiz A. Arroyo A. Navarro M.Á. Morales M.L. Guevara-Ramirez P. Justo P. López-Muñoz N. Sánchez-Pina J. Alonso R. Selma M.V. Frutos-Lisón M.D. García-Villalba R. Tomás-Barberán F.A. Ayala R. Martínez-López J. Linares M. Microbiota-derived urolithin A in monoclonal gammopathies and multiple myeloma therapy Microbiome 2025 13 10.1186/s40168-025-02045-6 PMC11869585 40022244

39 Zhang C. Sha Y. Liu H. Guo D. Jiang Y. Hong L. Shi L. Huang H. Type 2 diabetes mellitus does not increase the risk of multiple myeloma: a systematic review and meta-analysis Transl. Cancer Res. 9 2020 2884 10.21037/tcr.2020.03.36 35117645 PMC8798954

40 Zhang C.Y. Zhang D. Sun W.R. Tang H.L. Tian B. Hu L.H. Hu W.Y. Gao Y.Y. Li M.Y. Xiao W.T. Gao S. Gao G.X. Causal associations between the gut microbiota and multiple myeloma: a two-sample mendelian randomization study Front. Nutr. 11 2024 1400116 10.3389/fnut.2024.1400116 PMC11212462 38946785

41 McShane C.M. Murray L.J. Landgren O. O’Rorke M.A. Korde N. Kunzmann A.T. Ismail M.R. Anderson L.A. Prior autoimmune disease and risk of monoclonal gammopathy of undetermined significance and multiple myeloma: a systematic review Cancer Epidemiol. Biomarkers Prevent. 23 2014 332 342 10.1158/1055-9965.EPI-13-0695/345345/P/PRIOR-AUTOIMMUNE-DISEASE-AND-RISK-OF-MONOCLONAL 24451437

42 Jin P. Jin X. He L. Liu W. Zhan Z. The casual relationship between autoimmune diseases and multiple myeloma: a Mendelian randomization study Clin. Exp. Med. 24 2024 65 10.1007/s10238-024-01327-x 38564026 PMC10987346

43 Hemminki K. Liu X. Försti A. Ji J. Sundquist J. Sundquist K. Effect of autoimmune diseases on incidence and survival in subsequent multiple myeloma J. Hematol. Oncol. 5 1 2012 10.1186/1756-8722-5-59 2012559- PMC3473243 23031386

44 Khan M.A. Murugesan V. Latif H. Alhaj Moustafa M. Association between monoclonal gammopathy and rheumatoid arthritis: a national inpatient sample (NIS) study Blood 132 2018 5599 10.1182/blood-2018-99-115672 –5599

45 Nikolaos Andreas Chrysanthakopoulos, and E.V Investigation of periodontal condition, and tooth loss in multiple myeloma patients: a case-control study Journal of Dentistry and Oral Epidemiology 4 2024 10.54289/jdoe2400102

46 Rosenberg P.S. Barker K.A. Anderson W.F. Future distribution of multiple myeloma in the United States by sex, age, and race/ethnicity Blood 125 2015 410 10.1182/BLOOD-2014-10-609461 25573972 PMC4287646

47 Boyd K.D. Ross F.M. Chiecchio L. Dagrada G.P. Konn Z.J. Tapper W.J. Walker B.A. Wardell C.P. Gregory W.M. Szubert A.J. Davies F.E. Morgan G.J. Gender disparities in the tumor genetics and clinical outcome of multiple myeloma Cancer Epidemiol. Biomarkers Prevent. 20 2011 1703 1707 10.1158/1055-9965.EPI-11-0157/66426/AM/GENDER-DISPARITIES-IN-THE-TUMOR-GENETICS-AND PMC4545514 21680536

48 Benjamin M. Reddy S. Brawley O.W. Myeloma and race: a review of the literature Cancer Metastasis Rev. 22 2003 87 93 10.1023/A:1022268103136/METRICS 12716040

49 Lu J. Lu J. Chen W. Huo Y. Huang X. Hou J. Clinical features and treatment outcome in newly diagnosed Chinese patients with multiple myeloma: results of a multicenter analysis Blood Cancer J. 4 8 2014 e239–e239 10.1038/bcj.2014.55 20144 PMC4219472 25127393

50 Raghupathy R. Ayyappan S. Prabhakar D. Campagnaro E.L. Mo F.K.F. Li W. Hui E.P. Chan A.T.C. Lei K.I.K. Retrospective study of the incidence and patterns of arterial and venous thrombosis in Chinese versus African American patients with multiple myeloma Br. J. Haematol. 176 2017 315 317 10.1111/bjh.13942 26814167

51 Lakshman A. Vincent Rajkumar S. Buadi F.K. Binder M. Gertz M.A. Lacy M.Q. Dispenzieri A. Dingli D. Fonder A.L. Hayman S.R. Hobbs M.A. Gonsalves W.I. Hwa Y.L. Kapoor P. Leung N. Go R.S. Lin Y. Kourelis T.V. Warsame R. Lust J.A. Russell S.J. Zeldenrust S.R. Kyle R.A. Kumar S.K. Risk stratification of smoldering multiple myeloma incorporating revised IMWG diagnostic criteria Blood Cancer J. 8 6 2018 10.1038/s41408-018-0077-4 2018859- PMC5997745 29895887

52 Altieri A. Chen B. Bermejo J.L. Castro F. Hemminki K. Familial risks and temporal incidence trends of multiple myeloma Eur. J. Cancer 42 2006 1661 1670 10.1016/j.ejca.2005.11.033 16753294

53 Vachon C.M. Kyle R.A. Therneau T.M. Foreman B.J. Larson D.R. Colby C.L. Phelps T.K. Dispenzieri A. Kumar S.K. Katzmann J.A. Rajkumar S.V. Increased risk of monoclonal gammopathy in first-degree relatives of patients with multiple myeloma or monoclonal gammopathy of undetermined significance Blood 114 2009 785 790 10.1182/BLOOD-2008-12-192575 19179466 PMC2716020

54 Hannaford A. Del Bello D. Leng S. Chari A. Perumalswami P. Dieterich D. Branch A. Management of patients with Hepatitis C virus, monoclonal gammopathy of undetermined significance, and multiple myeloma J. Investig. Med. High. Impact. Case Rep. 5 2017 10.1177/2324709617696854 PMC5510768 28748190

55 Li N. Johnson D.C. Weinhold N. Kimber S. Dobbins S.E. Mitchell J.S. Kinnersley B. Sud A. Law P.J. Orlando G. Scales M. Wardell C.P. Försti A. Hoang P.H. Went M. Holroyd A. Hariri F. Pastinen T. Meissner T. Goldschmidt H. Hemminki K. Morgan G.J. Kaiser M. Houlston R.S. Genetic predisposition to multiple myeloma at 5q15 is mediated by an ELL2 enhancer polymorphism Cell Rep. 20 2017 2556 2564 10.1016/j.celrep.2017.08.062 28903037 PMC5608969

56 Rodríguez-García A. Mennesson N. Hernandez-Ibarburu G. Morales M.L. Garderet L. Bouchereau L. Allain-Maillet S. Piver E. Marbán I. Rubio D. Bigot-Corbel E. Martínez-López J. Linares M. Hermouet S. Impact of viral hepatitis therapy in multiple myeloma and other monoclonal gammopathies linked to hepatitis B or C viruses Haematologica 109 2024 272 282 10.3324/haematol.2023.283096 37199121 PMC10772493

57 George M.Y. Gamal N.K. Mansour D.E. Famurewa A.C. Bose D. Messiha P.A. Cerchione C. The gut microbiome role in multiple myeloma: emerging insights and therapeutic opportunities Hematol. Rep. 17 2025 56 10.3390/hematolrep17060056 2025Page1756 41283232 PMC12641986

58 Pianko M.J. Devlin S.M. Littmann E.R. Chansakul A. Mastey D. Salcedo M. Fontana E. Ling L. Tavitian E. Slingerland J.B. Slingerland A.E. Clurman A. Gomes A.L.C. Taur Y. Pamer E.G. Peled J.U. van den Brink M.R.M. Landgren O. Lesokhin A.M. Minimal residual disease negativity in multiple myeloma is associated with intestinal microbiota composition Blood Adv. 3 2019 2040 2044 10.1182/bloodadvances.2019032276 31289031 PMC6616258

59 Deng C. Li W. Fei Y. Li Y. Zhang F. Risk of malignancy in ankylosing spondylitis: a systematic review and meta-analysis Sci. Rep. 6 1 2016 10.1038/srep32063 2016632063 PMC4989281 27534810

60 Castro F. Parikh R. Eustaquio J.C. Derkach A. Joseph J.M. Lesokhin A.M. Usmani S.Z. Shah U.A. Pre-diagnosis dietary patterns and risk of multiple myeloma in the NIH-AARP diet and health study Leukemia 38 2024 438 441 10.1038/s41375-023-02132-3 38158443 PMC10919351

61 Shah U.A. Maclachlan K.H. Derkach A. Salcedo M. Barnett K. Caple J. Blaslov J. Tran L. Ciardiello A. Burge M. Shekarkhand T. Adintori P. Cross J. Pianko M.J. Hosszu K. McAvoy D. Mailankody S. Korde N. Hultcrantz M. Hassoun H. Tan C.R. Lu S.X. Patel D. Diamond B. Shah G. Scordo M. Lahoud O. Chung D.J. Landau H. Usmani S.Z. Giralt S. Taur Y. Landgren C.O. Block G. Block T. Peled J.U. van den Brink M.R.M. Lesokhin A.M. Sustained minimal residual disease negativity in multiple myeloma is associated with stool butyrate and healthier plant-based diets Clin. Cancer Res. 28 2022 5149 5155 10.1158/1078-0432.CCR-22-0723 36170461 PMC9722533

62 Bodnar S. Brander T. Gold J. Iverson A. Lagana A. Onel K. Jagannath S. Parekh S. Thibaud S. FaMMily Affairs: dissecting inherited contributions to multiple myeloma risk Semin. Hematol. 62 2025 11 19 10.1053/j.seminhematol.2024.11.006 39721861 PMC12008747

63 Pertesi M. Went M. Hansson M. Hemminki K. Houlston R.S. Nilsson B. Genetic predisposition for multiple myeloma Leukemia 34 3 2020 697 708 10.1038/s41375-019-0703-6 202034 31913320

64 Sternbach N. Gertz M.A. Familial aspects of multiple myeloma and Waldenström macroglobulinemia: understanding the predisposition in relatives and the importance of early diagnosis Leuk. Lymphoma 66 2025 1801 1808 10.1080/10428194.2025.2516256;ISSUE:ISSUE:DOI 40512670

65 Hakkarainen M. Koski J.R. Heckman C.A. Anttila P. Silvennoinen R. Lievonen J. Kilpivaara O. Wartiovaara-Kautto U. A germline exome analysis reveals harmful POT1 variants in multiple myeloma patients and families EJHaem. 3 2022 1352 1357 10.1002/JHA2.557 36467798 PMC9713058

66 Pertesi M. Vallée M. Wei X. Revuelta M.V. Galia P. Demangel D. Oliver J. Foll M. Chen S. Perrial E. Garderet L. Corre J. Leleu X. Boyle E.M. Decaux O. Rodon P. Kolb B. Slama B. Mineur P. Voog E. Le Bris C. Fontan J. Maigre M. Beaumont M. Azais I. Sobol H. Vignon M. Royer B. Perrot A. Fuzibet J.G. Dorvaux V. Anglaret B. Cony-Makhoul P. Berthou C. Desquesnes F. Pegourie B. Leyvraz S. Mosser L. Frenkiel N. Augeul-Meunier K. Leduc I. Leyronnas C. Voillat L. Casassus P. Mathiot C. Cheron N. Paubelle E. Moreau P. Bignon Y.–J Joly B. Bourquard P. Caillot D. Naman H. Rigaudeau S. Marit G. Macro M. Lambrecht I. Cliquennois M. Vincent L. Helias P. Avet-Loiseau H. Moreno V. Reis R.M. Varkonyi J. Kruszewski M. Vangsted A.J. Jurczyszyn A. Zaucha J.M. Sainz J. Krawczyk-Kulis M. Wątek M. Pelosini M. Iskierka-Jażdżewska E. Grząśko N. Martinez-Lopez J. Jerez A. Campa D. Buda G. Lesueur F. Dudziński M. García-Sanz R. Nagler A. Rymko M. Jamroziak K. Butrym A. Canzian F. Obazee O. Nilsson B. Klein R.J. Lipkin S.M. McKay J.D. Dumontet C. Exome sequencing identifies germline variants in DIS3 in familial multiple myeloma Leukemia 33 9 2019 2324 2330 10.1038/s41375-019-0452-6 201933 30967618 PMC6756025

67 Martins Rodrigues F. Jasielec J. Perpich M. Kim A. Moma L. Li Y. Storrs E. Wendl M.C. Jayasinghe R.G. Fiala M. Stefka A. Derman B. Jakubowiak A.J. DiPersio J.F. Vij R. Godley L.A. Ding L. Germline predisposition in multiple myeloma iScience 28 2025 111620 10.1016/J.ISCI.2024.111620 PMC11750583 39845416

68 del Rosal M.V. Bodnar S. Brander T. Katz M. Genthe W. Onel K. Jagannath S. Lagana A. Parekh S. Thibaud S. Selective germline testing in multiple myeloma patients yields a six-fold increase in actionable cancer predisposition findings: time for screening guidelines? Blood 146 2025 2151 10.1182/BLOOD-2025-2151 –2151

69 Thibaud S. Etra A. Subaran R. Soens Z. Newman S. Chen R. Chari A. Cho H.J. Jagannath S. Madduri D. Melnekoff D.T. Richard S. Richter J. Sanchez L. Huang K. Lagana A. Parekh S. Onel K. Pathogenic germline variants in multiple myeloma Blood 138 2021 399 10.1182/BLOOD-2021-151495 –399

70 Thibaud S. Genthe W. Bodnar S. Lagana A. Houldsworth J. Chari A. Rossi A.C. Rodriguez C. Sanchez L.J. Richard S. Richter J. Onel K. Jagannath S. Brander T. Parekh S. Confirmed pathogenic germline variants in cancer predisposition genes incidentally detected in somatic genomic profiling of multiple myeloma patients Blood 142 2023 534 10.1182/BLOOD-2023-190878 –534

71 Kubicki T. Millan K. Pula A. Raeder H. Hauck Q. Bhagwat M. Barton J. Awe E. Das S. Del Gaudio D. Wu S. Wen F. Cooperrider J.H. Derman B.A. Jakubowiak A.J. Drazer M.W. Prevalence and clinical implications of germline variants in multiple Myeloma patients Blood 144 2024 3335 10.1182/BLOOD-2024-207445 –3335

72 Rand K.A. Song C. Dean E. Serie D.J. Curtin K. Sheng X. Hu D. Huff C.A. Bernal-Mizrachi L. Tomasson H.M. Ailawadhi S. Singhal S. Pawlish K. Peters E.S. Bock C.H. Stram A. Van Den Berg D.J. Edlund C.K. Conti D.V. Zimmerman T. Hwang A.E. Huntsman S. Graff J. Nooka A. Kong Y. Pregja S.L. Berndt S.I. Blot W.J. Carpten J. Casey G. Chu L. Diver W.R. Stevens V.L. Lieber M.R. Goodman P.J. Hennis A.J.M. Hsing A.W. Mehta J. Kittles R.A. Kolb S. Klein E.A. Leske C. Murphy A.B. Nemesure B. Neslund-Dudas C. Strom S.S. Vij R. Rybicki B.A. Stanford J.L. Signorello L.B. Witte J.S. Ambrosone C.B. Bhatti P. John E.M. Bernstein L. Zheng W. Olshan A.F. Hu J.J. Ziegler R.G. Nyante S.J. Bandera E.V. Birmann B.M. Ingles S.A. Press M.F. Atanackovic D. Glenn M.J. Cannon-Albright L.A. Jones B. Tricot G. Martin T.G. Kumar S.K. Wolf J.L. Deming Halverson S.L. Rothman N. Brooks-Wilson A.R. Rajkumar S.V. Kolonel L.N. Chanock S.J. Slager S.L. Severson R.K. Janakiraman N. Terebelo H.R. Brown E.E. De Roos A.J. Mohrbacher A.F. Colditz G.A. Giles G.G. Spinelli J.J. Chiu B.C. Munshi N.C. Anderson K.C. Levy J. Zonder J.A. Orlowski R.Z. Lonial S. Camp N.J. Vachon C.M. Ziv E. Stram D.O. Hazelett D.J. Haiman C.A. Cozen W. A meta-analysis of multiple myeloma risk regions in African and European ancestry populations identifies putatively functional loci Cancer Epidemiol. Biomarkers Prevent. 25 2016 1609 1618 10.1158/1055-9965.EPI-15-1193 PMC5524541 27587788

73 Shen K. Xu G. Wu Q. Zhou D. Li J. Risk of multiple myeloma in rheumatoid arthritis: a meta-analysis of case-control and cohort studies PLoS. One 9 2014 e91461 10.1371/journal.pone.0091461 PMC3953405 24626254

74 Weinhold N. Meissner T. Johnson D.C. Seckinger A. Moreaux J. Försti A. Chen B. Nickel J. Chubb D. Rawstron A.C. Doughty C. Dahir N.B. Begum D.B. Young K. Walker B.A. Hoffmann P. Nöthen M.M. Davies F.E. Klein B. Goldschmidt H. Morgan G.J. Houlston R.S. Hose D. Hemminki K. The 7p15.3 (rs4487645) association for multiple myeloma shows strong allele-specific regulation of the MYC-interacting gene CDCA7L in malignant plasma cells Haematologica 100 2015 e110–e113 10.3324/HAEMATOL.2014.118786 PMC4349291 25480495

75 Mitchell J.S. Li N. Weinhold N. Försti A. Ali M. Van Duin M. Thorleifsson G. Johnson D.C. Chen B. Halvarsson B.M. Gudbjartsson D.F. Kuiper R. Stephens O.W. Bertsch U. Broderick P. Campo C. Einsele H. Gregory W.A. Gullberg U. Henrion M. Hillengass J. Hoffmann P. Jackson G.H. Johnsson E. Jöud M. Kristinsson S.Y. Lenhoff S. Lenive O. Mellqvist U.H. Migliorini G. Nahi H. Nelander S. Nickel J. Nöthen M.M. Rafnar T. Ross F.M. Da Silva Filho M.I. Swaminathan B. Thomsen H. Turesson I. Vangsted A. Vogel U. Waage A. Walker B.A. Wihlborg A.K. Broyl A. Davies F.E. Thorsteinsdottir U. Langer C. Hansson M. Kaiser M. Sonneveld P. Stefansson K. Morgan G.J. Goldschmidt H. Hemminki K. Nilsson B. Houlston R.S. Genome-wide association study identifies multiple susceptibility loci for multiple myeloma Nat. Commun. 7 1 2016 10.1038/ncomms12050 2016712050- PMC4932178 27363682

76 Chattopadhyay S. Thomsen H. Yadav P. da Silva Filho M.I. Weinhold N. Nöthen M.M. Hoffman P. Bertsch U. Huhn S. Morgan G.J. Goldschmidt H. Houlston R. Hemminki K. Försti A. Genome-wide interaction and pathway-based identification of key regulators in multiple myeloma Commun. Biol. 2 1 2019 10.1038/s42003-019-0329-2 2019289- PMC6399257 30854481

77 Went M. Sud A. Försti A. Halvarsson B.M. Weinhold N. Kimber S. van Duin M. Thorleifsson G. Holroyd A. Johnson D.C. Li N. Orlando G. Law P.J. Ali M. Chen B. Mitchell J.S. Gudbjartsson D.F. Kuiper R. Stephens O.W. Bertsch U. Broderick P. Campo C. Bandapalli O.R. Einsele H. Gregory W.A. Gullberg U. Hillengass J. Hoffmann P. Jackson G.H. Jöckel K.H. Johnsson E. Kristinsson S.Y. Mellqvist U.H. Nahi H. Easton D. Pharoah P. Dunning A. Peto J. Canzian F. Swerdlow A. Eeles R.A. Kote-Jarai Zs.S. Muir K. Pashayan N. Nickel J. Nöthen M.M. Rafnar T. Ross F.M. da Silva Filho M.I. Thomsen H. Turesson I. Vangsted A. Andersen N.F. Waage A. Walker B.A. Wihlborg A.K. Broyl A. Davies F.E. Thorsteinsdottir U. Langer C. Hansson M. Goldschmidt H. Kaiser M. Sonneveld P. Stefansson K. Morgan G.J. Hemminki K. Nilsson B. Houlston R.S. Henderson B.E. Haiman C.A. Benlloch S. Schumacher F.R. Olama A.A.Al Berndt S.I. Conti D.V. Wiklund F. Chanock S. Stevens V.L. Tangen C.M. Batra J. Clements J. Gronberg H. Schleutker J. Albanes D. Weinstein S. Wolk A. West C. Mucci L. Cancel-Tassin G. Koutros S. Sorensen K.D. Grindedal E.M. Neal D.E. Hamdy F.C. Donovan J.L. Travis R.C. Hamilton R.J. Ingles S.A. Rosenstein B. Lu Y.J. Giles G.G. Kibel A.S. Vega A. Kogevinas M. Penney K.L. Park J.Y. Stanford J.L. Cybulski C. Nordestgaard B.G. Brenner H. Maier C. Kim J. John E.M. Teixeira M.R. Neuhausen S.L. De Ruyck K. Razack A. Newcomb L.F. Lessel D. Kaneva R. Usmani N. Claessens F. Townsend P.A. Dominguez M.G. Roobol M.J. Menegaux F. Khaw K.T. Cannon-Albright L. Pandha H. Thibodeau S.N. Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma Nat. Commun. 9 1 2018 3707 10.1038/s41467-018-04989-w 20189- 30213928 PMC6137048

78 Ziv E. Dean E. Hu D. Martino A. Serie D. Curtin K. Campa D. Aftab B. Bracci P. Buda G. Zhao Y. Caswell-Jin J. Diasio R. Dumontet C. Dudziński M. Fejerman L. Greenberg A. Huntsman S. Jamroziak K. Jurczyszyn A. Kumar S. Atanackovic D. Glenn M. Cannon-Albright L.A. Jones B. Lee A. Marques H. Martin T. Martinez-Lopez J. Rajkumar V. Sainz J. Vangsted A.J. Wątek M. Wolf J. Slager S. Camp N.J. Canzian F. Vachon C. Genome-wide association study identifies variants at 16p13 associated with survival in multiple myeloma patients Nat. Commun. 6 2015 10.1038/NCOMMS8539 PMC4656791 26198393

79 Campa D. Martino A. Varkonyi J. Lesueur F. Jamroziak K. Landi S. Jurczyszyn A. Marques H. Andersen V. Jurado M. Brenner H. Petrini M. Vogel U. García-Sanz R. Buda G. Gemignani F. Ríos R. Vangsted A.J. Dumontet C. Martínez-López J. Moreno M.J. Sterpień A. Wątek M. Moreno V. Dieffenbach A.K. Rossi A.M. Butterbach K. Jacobsen S.E.H. Goldschmidt H. Sainz J. Hillengass J. Orciuolo E. Dudziński M. Weinhold N. Reis R.M. Canzian F. Risk of multiple myeloma is associated with polymorphisms within telomerase genes and telomere length Int. J. Cancer 136 2015 E351–E358 10.1002/ijc.29101 25066524

80 Canzian F. Piredda C. Macauda A. Zawirska D. Andersen N.F. Nagler A. Zaucha J.M. Mazur G. Dumontet C. Wątek M. Jamroziak K. Sainz J. Várkonyi J. Butrym A. Beider K. Abildgaard N. Lesueur F. Dudziński M. Vangsted A.J. Pelosini M. Subocz E. Petrini M. Buda G. Raźny M. Gemignani F. Marques H. Orciuolo E. Kadar K. Jurczyszyn A. Druzd-Sitek A. Vogel U. Andersen V. Reis R.M. Suska A. Avet-Loiseau H. Kruszewski M. Tomczak W. Rymko M. Minvielle S. Campa D. A polygenic risk score for multiple myeloma risk prediction Eur. J. Human Genetics 30 4 2021 474 479 10.1038/s41431-021-00986-8 202130 34845334 PMC8991223

81 Kullo I.J. Clinical use of polygenic risk scores: current status, barriers and future directions Nat. Rev. Genet. 27 3 2025 246 263 10.1038/s41576-025-00900-8 202527 41073616

82 Lewis C.M. Vassos E. Polygenic risk scores: from research tools to clinical instruments Genome Med. 12 1 2020 10.1186/S13073-020-00742-5 20201244- PMC7236300 32423490

83 Wang Y. Tsuo K. Kanai M. Neale B.M. Martin A.R. Challenges and opportunities for developing more generalizable polygenic risk scores Annu Rev. Biomed. Data Sci. 5 2022 293 320 10.1146/ANNUREV-BIODATASCI-111721-074830/CITE/REFWORKS 35576555 PMC9828290

84 Cooper R.S. Tayo B. Zhu X. Genome-wide association studies: implications for multiethnic samples Hum. Mol. Genet. 17 2008 10.1093/HMG/DDN263 R151–R155 PMC2782359 18852204

85 Peterson R.E. Kuchenbaecker K. Walters R.K. Chen C.Y. Popejoy A.B. Periyasamy S. Lam M. Iyegbe C. Strawbridge R.J. Brick L. Carey C.E. Martin A.R. Meyers J.L. Su J. Chen J. Edwards A.C. Kalungi A. Koen N. Majara L. Schwarz E. Smoller J.W. Stahl E.A. Sullivan P.F. Vassos E. Mowry B. Prieto M.L. Cuellar-Barboza A. Bigdeli T.B. Edenberg H.J. Huang H. Duncan L.E. Genome-wide Association Studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations Cell 179 2019 589 603 10.1016/j.cell.2019.08.051 31607513 PMC6939869

86 Tam V. Patel N. Turcotte M. Bossé Y. Paré G. Meyre D. Benefits and limitations of genome-wide association studies Nat. Rev. Genet. 20 2019 467 484 10.1038/S41576-019-0127-1;SUBJMETA 31068683

87 Cheng Y. Sun F. Cui X. Janz S. Genetic predisposition to multiple myeloma Blood Genom. 4 2020 9 18 10.46701/BG.2020012020103

88 Janz S. Zhan F. Sun F. Cheng Y. Pisano M. Yang Y. Goldschmidt H. Hari P. Germline risk contribution to genomic instability in multiple myeloma Front. Genet. 10 2019 456173 10.3389/FGENE.2019.00424/FULL PMC6518313 31139207

89 Manna L. Gelsomino L. Martino E.A. Gentile M. Andò S. Bonofiglio D. Giordano C. Catalano S. Barone I. Unraveling obesity and multiple myeloma: insights from epidemiology and molecular mechanisms Curr. Obes. Rep. 14 1 2025 10.1007/S13679-025-00644-W 20251452- PMC12137537 40468124

90 Ram M. Fraser M.R. Dos Santos J.V. Tasakis R. Islam A. Abo-Donia J.U. Parekh S. Lagana A. The genetic and molecular drivers of Multiple myeloma: current insights, clinical implications, and the path forward Pharmgenomics. Pers. Med. 17 2024 573 609 10.2147/PGPM.S350238 39723112 PMC11669356

91 Maura F. Samur M.K. Munshi N.C. Evolution of multiple myeloma from a genomic perspective Blood 147 2026 1423 1432 10.1182/BLOOD.2024026313 41359792 PMC13077438

92 Abdallah N. Rajkumar S.V. Greipp P. Kapoor P. Gertz M.A. Dispenzieri A. Baughn L.B. Lacy M.Q. Hayman S.R. Buadi F.K. Dingli D. Go R.S. Hwa Y.L. Fonder A. Hobbs M. Lin Y. Leung N. Kourelis T. Warsame R. Siddiqui M. Lust J. Kyle R.A. Bergsagel L. Ketterling R. Kumar S.K. Cytogenetic abnormalities in multiple myeloma: association with disease characteristics and treatment response Blood Cancer J. 10 8 2020 10 10.1038/s41408-020-00348-5 202082- 32782240 PMC7419564

93 Gabrea A. Leif Bergsagel P. Michael Kuehl W. Distinguishing primary and secondary translocations in multiple myeloma DNa Repair. (Amst) 5 2006 1225 1233 10.1016/j.dnarep.2006.05.012 16829212

94 Kadam Amare P. Nikalje Khasnis S. Hande P. Lele H. Wable N. Kaskar S. Nikam Gujar N. Gardi N. Prabhudesai A. Todi K. Waghole R. Roy P. Cytogenetic abnormalities in multiple myeloma: incidence, prognostic significance, and geographic heterogeneity in Indian and Western populations Cytogenet. Genome Res. 162 2023 529 10.1159/000529191 PMC10534967 36780889

95 Rajan A.M. Rajkumar S.V. Interpretation of cytogenetic results in multiple myeloma for clinical practice Blood Cancer J. 5 2015 e365 10.1038/BCJ.2015.92 PMC4635200 26517360

96 Leif Bergsagel P. Michael Kuehl W. Chromosome translocations in multiple myeloma Oncogene 20 2001 5611 5622 10.1038/SJ.ONC.1204641 11607813

97 Avet-Loiseau H. Brigaudeau C. Morineau N. Talmant P. Laï J.L. Daviet A. Li J.Y. Praloran V. Rapp M.J. Harousseau J.L. Facon T. Bataille R. High incidence of cryptic translocations involving the ig heavy chain gene in multiple myeloma, as shown by fluorescence in situ hybridization Genes Chromosomes Cancer 24 1999 9 15 10.1002/(SICI)1098-2264(199901)24:1<9::AID-CC2>3.0.CO;2-K 9892103

98 Fonseca R. Witzig T.E. Gertz M.A. Kyle R.A. Hoyer J.D. Jalal S.M. Greipp P.R. Multiple myeloma and the translocation t(11;14)(q13;q32): a report on 13 cases Br. J. Haematol. 101 1998 296 301 10.1046/J.1365-2141.1998.00700.X 9609525

99 Diamantidis M.D. Papadaki S. Hatjiharissi E. Exploring the current molecular landscape and management of multiple myeloma patients with the t(11;14) translocation Front. Oncol. 12 2022 934008 10.3389/fonc.2022.934008 PMC9379277 35982976

100 Moreau P. Facon T. Leleu X. Morineau N. Huyghe P. Harousseau J.L. Bataille R. Avet-Loiseau H. Recurrent 14q32 translocations determine the prognosis of multiple myeloma, especially in patients receiving intensive chemotherapy Blood 100 2002 1579 1583 10.1182/BLOOD-2002-03-0749 12176874

101 Mikhael J.R. Dingli D. Roy V. Reeder C.B. Buadi F.K. Hayman S.R. Dispenzieri A. Fonseca R. Sher T. Kyle R.A. Lin Y. Russell S.J. Kumar S. Bergsagel P.L. Zeldenrust S.R. Leung N. Drake M.T. Kapoor P. Ansell S.M. Witzig T.E. Lust J.A. Dalton R.J. Gertz M.A. Stewart K. Rajkumar S.V. Chanan-Khan A. Lacy M.Q. Management of newly diagnosed symptomatic multiple myeloma: updated mayo stratification of myeloma and risk-adapted therapy (msmart) consensus guidelines 2013 Mayo Clin. Proc. 88 2013 360 376 10.1016/j.mayocp.2013.01.019 23541011

102 Chakraborty R. Bhutani D. Lentzsch S. How do we manage t(11;14) plasma cell disorders with venetoclax? Br. J. Haematol. 199 2022 31 39 10.1111/BJH.18243 35594184

103 Kalff A. Spencer A. The t(4;14) translocation and FGFR3 overexpression in multiple myeloma: prognostic implications and current clinical strategies Blood Cancer J. 2 9 2012 e89–e89 10.1038/bcj.2012.37 20122 PMC3461707 22961061

104 Narita T. Inagaki A. Kobayashi T. Kuroda Y. Fukushima T. Nezu M. Fuchida S. Sakai H. Sekiguchi N. Sugiura I. Maeda Y. Takamatsu H. Tsukamoto N. Maruyama D. Kubota Y. Kojima M. Sunami K. Ono T. Ri M. Tobinai K. Iida S. t(14;16)-positive multiple myeloma shows negativity for CD56 expression and unfavorable outcome even in the era of novel drugs Blood Cancer J. 5 2015 10.1038/BCJ.2015.6 PMC4349263 25723856

105 Shaughnessy J. Gabrea A. Qi Y. Brents L. Zhan F. Tian E. Sawyer J. Barlogie B. Leif Bergsagel P. Kuehl M. Cyclin D3 at 6p21 is dysregulated by recurrent chromosomal translocations to immunoglobulin loci in multiple myeloma Blood 98 2001 217 223 10.1182/BLOOD.V98.1.217 11418483

106 Hurt E.M. Wiestner A. Rosenwald A. Shaffer A.L. Campo E. Grogan T. Bergsagel P.L. Kuehl W.M. Staudt L.M. Overexpression of c-maf is a frequent oncogenic event in multiple myeloma that promotes proliferation and pathological interactions with bone marrow stroma Cancer Cell 5 2004 191 199 10.1016/S1535-6108(04)00019-4 14998494

107 Avet-Loiseau H. Malard F. Campion L. Magrangeas F. Sebban C. Lioure B. Decaux O. Lamy T. Legros L. Fuzibet J.G. Michallet M. Corront B. Lenain P. Hulin C. Mathiot C. Attal M. Facon T. Harousseau J.L. Minvielle S. Moreau P. Translocation t(14;16) and multiple myeloma: is it really an independent prognostic factor? Blood 117 2011 2009 2011 10.1182/BLOOD-2010-07-295105 20962323

108 Walker B.A. Wardell C.P. Murison A. Boyle E.M. Begum D.B. Dahir N.M. Proszek P.Z. Melchor L. Pawlyn C. Kaiser M.F. Johnson D.C. Qiang Y.W. Jones J.R. Cairns D.A. Gregory W.M. Owen R.G. Cook G. Drayson M.T. Jackson G.H. Davies F.E. Morgan G.J. APOBEC family mutational signatures are associated with poor prognosis translocations in multiple myeloma Nat. Commun. 6 2015 10.1038/NCOMMS7997 PMC4568299 25904160

109 Zhan F. Huang Y. Colla S. Stewart J.P. Hanamura I. Gupta S. Epstein J. Yaccoby S. Sawyer J. Burington B. Anaissie E. Hollmig K. Pineda-Roman M. Tricot G. Van Rhee F. Walker R. Zangari M. Crowley J. Barlogie B. Shaughnessy J.D. The molecular classification of multiple myeloma Blood 108 2006 2020 2028 10.1182/BLOOD-2005-11-013458 16728703 PMC1895543

110 Avet-Loiseau H. Gerson F. Magrangeas F. Minvielle S. Harousseau J.L. Bataille R. Rearrangements of the c-myc oncogene are present in 15% of primary human multiple myeloma tumors Blood 98 2001 3082 3086 10.1182/BLOOD.V98.10.3082 11698294

111 Avet-Loiseau H. Attal M. Moreau P. Charbonnel C. Garban F. Hulin C. Leyvraz S. Michallet M. Yakoub-Agha I. Garderet L. Marit G. Michaux L. Voillat L. Renaud M. Grosbois B. Guillerm G. Benboubker L. Monconduit M. Thieblemont C. Casassus P. Caillot D. Stoppa A.M. Sotto J.J. Wetterwald M. Dumontet C. Fuzibet J.G. Azais I. Dorvaux V. Zandecki M. Bataille R. Minvielle S. Harousseau J.L. Facon T. Mathiot C. Genetic abnormalities and survival in multiple myeloma: the experience of the Intergroupe Francophone du Myélome Blood 109 2007 3489 3495 10.1182/BLOOD-2006-08-040410 17209057

112 Jian Y. Chen X. Zhou H. Zhu W. Liu N. Geng C. Chen W. Prognostic impact of cytogenetic abnormalities in multiple myeloma: a retrospective analysis of 229 patients Medicine (Baltimore) 95 2016 10.1097/MD.0000000000003521 PMC4902489 27175647

113 Pawlyn C. Melchor L. Murison A. Wardell C.P. Brioli A. Boyle E.M. Kaiser M.F. Walker B.A. Begum D.B. Dahir N.B. Proszek P. Gregory W.M. Drayson M.T. Jackson G.H. Ross F.M. Davies F.E. Morgan G.J. Coexistent hyperdiploidy does not abrogate poor prognosis in myeloma with adverse cytogenetics and may precede IGH translocations Blood 125 2014 831 10.1182/BLOOD-2014-07-584268 25428216 PMC4327151

114 Smadja N.V. Bastard C. Brigaudeau C. Leroux D. Fruchart C. Hypodiploidy is a major prognostic factor in multiple myeloma Blood 98 2001 2229 2238 10.1182/BLOOD.V98.7.2229 11568011

115 Shaughnessy J. Amplification and overexpression of CKS1B at chromosome band 1q21 is associated with reduced levels of p27Kip1 and an aggressive clinical course in multiple myeloma Hematology. 10 1 2005 117 126 10.1080/10245330512331390140 Suppl 16188652

116 Boyd K.D. Ross F.M. Walker B.A. Wardell C.P. Tapper W.J. Chiecchio L. Dagrada G.P. Konn Z.J. Gregory W.M. Jackson G.H. Child J.A. Davies F.E. Morgan G.J. Mapping of chromosome 1p deletions in myeloma identifies FAM46C at 1p12 and CDKN2C at 1p32.3 as being genes in regions associated with adverse survival Clin. Cancer Res. 17 2011 7776 7784 10.1158/1078-0432.CCR-11-1791 21994415 PMC5751883

117 Chng W.J. Kumar S. VanWier S. Ahmann G. Price-Troska T. Henderson K. Chung T.H. Kim S. Mulligan G. Bryant B. Carpten J. Gertz M. Rajkumar S.V. Lacy M. Dispenzieri A. Kyle R. Greipp P. Bergsagel P.L. Fonseca R. Molecular dissection of hyperdiploid multiple myeloma by gene expression profiling Cancer Res. 67 2007 2982 2989 10.1158/0008-5472.CAN-06-4046 17409404

118 Schmidt-Wolf I.G.H. Glasmacher A. Hahn-Ast C. Jüttner A. Schnurr T. Cremer F. Moehler T. Goldschmidt H. Busert B. Schubert R. Schwanitz G. Chromosomal aberrations in 130 patients with multiple myeloma studied by interphase FISH: diagnostic and prognostic relevance Cancer Genet. Cytogenet. 167 2006 20 25 10.1016/J.CANCERGENCYTO.2005.10.016 16682281

119 Hanamura I. Gain/amplification of chromosome arm 1q21 in multiple myeloma Cancers 13 2021 256 10.3390/CANCERS13020256 2021Page13256 33445467 PMC7827173

120 Hanamura I. Stewart J.P. Huang Y. Zhan F. Santra M. Sawyer J.R. Hollmig K. Zangarri M. Pineda-Roman M. Van Rhee F. Cavallo F. Burington B. Crowley J. Tricot G. Barlogie B. Shaughnessy J.D. Frequent gain of chromosome band 1q21 in plasma-cell dyscrasias detected by fluorescence in situ hybridization: incidence increases from MGUS to relapsed myeloma and is related to prognosis and disease progression following tandem stem-cell transplantati… Blood 108 2006 1724 10.1182/BLOOD-2006-03-009910 16705089 PMC1895503

121 Fonseca R. Van Wier S.A. Chng W.J. Ketterling R. Lacy M.Q. Dispenzieri A. Bergsagel P.L. Rajkumar S.V. Greipp P.R. Litzow M.R. Price-Troska T. Henderson K.J. Ahmann G.J. Gertz M.A. Prognostic value of chromosome 1q21 gain by fluorescent in situ hybridization and increase CKS1B expression in myeloma Leukemia 20 2006 2034 2040 10.1038/sj.leu.2404403 17024118

122 Marzin Y. Jamet D. Douet-Guilbert N. Morel F. Le Bris M.J. Morice P. Abgrall J.F. Berthou C. De Braekeleer M. Chromosome 1 abnormalities in multiple myeloma Anticancer Res. 2006 26 16619492

123 Chang H. Jiang A. Qi C. Trieu Y. Chen C. Reece D. Impact of genomic aberrations including chromosome 1 abnormalities on the outcome of patients with relapsed or refractory multiple myeloma treated with lenalidomide and dexamethasone Leuk. Lymphoma 51 2010 2084 2091 10.3109/10428194.2010.524325 20929319

124 Walker B.A. Leone P.E. Chiecchio L. Dickens N.J. Jenner M.W. Boyd K.D. Johnson D.C. Gonzalez D. Dagrada G.P. Protheroe R.K.M. Konn Z.J. Stockley D.M. Gregory W.M. Davies F.E. Ross F.M. Morgan G.J. A compendium of myeloma-associated chromosomal copy number abnormalities and their prognostic value Blood 116 2010 e56–e65 10.1182/BLOOD-2010-04-279596 20616218

125 Fonseca R. Oken M.M. Harrington D. Bailey R.J. Van Wier S.A. Henderson K.J. Kay N.E. Van Ness B. Greipp P.R. Dewald G.W. Deletions of chromosome 13 in multiple myeloma identified by interphase FISH usually denote large deletions of the q arm or monosomy Leukemia 15 6 2001 981 986 10.1038/sj.leu.2402125 200115 11417487

126 Boyd K.D. Ross F.M. Chiecchio L. Dagrada G.P. Konn Z.J. Tapper W.J. Walker B.A. Wardell C.P. Gregory W.M. Szubert A.J. Bell S.E. Child J.A. Jackson G.H. Davies F.E. Morgan G.J. A novel prognostic model in myeloma based on co-segregating adverse FISH lesions and the ISS: analysis of patients treated in the MRC Myeloma IX trial Leukemia 26 2012 349 355 10.1038/LEU.2011.204 21836613 PMC4545515

127 Neben K. Jauch A. Bertsch U. Heiss C. Hielscher T. Seckinger A. Mors T. Müller N.Z. Hillengass J. Raab M.S. Ho A.D. Hose D. Goldschmidt H. Combining information regarding chromosomal aberrations t(4;14) and del(17p13) with the International Staging System classification allows stratification of myeloma patients undergoing autologous stem cell transplantation Haematologica 95 2010 1150 10.3324/HAEMATOL.2009.016436 20220069 PMC2895040

128 Fonseca R. Blood E. Rue M. Harrington D. Oken M.M. Kyle R.A. Dewald G.W. Van Ness B. Van Wier S.A. Henderson K.J. Bailey R.J. Greipp P.R. Clinical and biologic implications of recurrent genomic aberrations in myeloma Blood 101 2003 4569 4575 10.1182/BLOOD-2002-10-3017 12576322

129 Tiedemann R.E. Gonzalez-Paz N. Kyle R.A. Santana-Davila R. Price-Troska T. Van Wier S.A. Chng W.J. Ketterling R.P. Gertz M.A. Henderson K. Greipp P.R. Dispenzieri A. Lacy M.Q. Rajkumar S.V. Bergsagel P.L. Stewart A.K. Fonseca R. Genetic aberrations and survival in plasma cell leukemia Leukemia 22 2008 1044 1052 10.1038/LEU.2008.4 18216867 PMC3893817

130 Manier S. Salem K.Z. Park J. Landau D.A. Getz G. Ghobrial I.M. Genomic complexity of multiple myeloma and its clinical implications Nat. Rev. Clin. Oncol. 14 2017 100 113 10.1038/NRCLINONC.2016.122 27531699

131 Palumbo A. Avet-Loiseau H. Oliva S. Lokhorst H.M. Goldschmidt H. Rosinol L. Richardson P. Caltagirone S. Lahuerta J.J. Facon T. Bringhen S. Gay F. Attal M. Passera R. Spencer A. Offidani M. Kumar S. Musto P. Lonial S. Petrucci M.T. Orlowski R.Z. Zamagni E. Morgan G. Dimopoulos M.A. Durie B.G.M. Anderson K.C. Sonneveld P. Miguel J.S. Cavo M. Rajkumar S.V. Moreau P. Revised International Staging system for Multiple Myeloma: a report from International Myeloma Working Group J. Clin. Oncol. 33 2015 2863 2869 10.1200/JCO.2015.61.2267 26240224 PMC4846284

132 Chesi M. Bergsagel P.L. Advances in the pathogenesis and diagnosis of multiple myeloma Int. J. Lab. Hematol. 37 2015 108 114 10.1111/ijlh.12360 25976968

133 Gutiérrez N.C. García J.L. Hernández Jesús M. Lumbreras E. Castellanos M. Rasillo A. Mateo G. Hernández José M. Pérez S. Orfao A. San Miguel J.F. Prognostic and biologic significance of chromosomal imbalances assessed by comparative genomic hybridization in multiple myeloma Blood 104 2004 2661 2666 10.1182/blood-2004-04-1319 15238415

134 Affer M. Chesi M. Chen W.D. Keats J.J. Demchenko Y.N. Tamizhmani K. Garbitt V.M. Riggs D.L. Brents L.A. Roschke A.V. Van Wier S. Fonseca R. Bergsagel P.L. Kuehl W.M. Promiscuous MYC locus rearrangements hijack enhancers but mostly super-enhancers to dysregulate MYC expression in multiple myeloma Leukemia 28 8 2014 1725 1735 10.1038/leu.2014.70 201428 24518206 PMC4126852

135 Weinhold N. Kirn D. Seckinger A. Hielscher T. Granzow M. Bertsch U. Egerer G. Salwender H. Blau I.W. Weisel K. Hillengass J. Raab M.S. Hose D. Goldschmidt H. Jauch A. Concomitant gain of 1q21 and MYC translocation define a poor prognostic subgroup of hyperdiploid multiple myeloma Haematologica 101 2016 e116–e119 10.3324/HAEMATOL.2015.136929 PMC4815741 26611471

136 Walker B.A. Wardell C.P. Brioli A. Boyle E. Kaiser M.F. Begum D.B. Dahir N.B. Johnson D.C. Ross F.M. Davies F.E. Morgan G.J. Translocations at 8q24 juxtapose MYC with genes that harbor superenhancers resulting in overexpression and poor prognosis in myeloma patients Blood Cancer J. 4 3 2014 e191–e191 10.1038/bcj.2014.13 20144 PMC3972699 24632883

137 Bolli N. Avet-Loiseau H. Wedge D.C. Van Loo P. Alexandrov L.B. Martincorena I. Dawson K.J. Iorio F. Nik-Zainal S. Bignell G.R. Hinton J.W. Li Y. Tubio J.M.C. McLaren S. O’Meara S. Butler A.P. Teague J.W. Mudie L. Anderson E. Rashid N. Tai Y.T. Shammas M.A. Sperling A.S. Fulciniti M. Richardson P.G. Parmigiani G. Magrangeas F. Minvielle S. Moreau P. Attal M. Facon T. Futreal P.A. Anderson K.C. Campbell P.J. Munshi N.C. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma Nat. Commun. 5 1 2014 10.1038/ncomms3997 201452997- PMC3905727 24429703

138 Chang H. Zhuang L. Nie E. Bouman D. Stewart A.K. Chun K. Detection of chromosome 13q deletions and IgH translocations in patients with multiple myeloma by FISH: comparison with karyotype analysis Leuk. Lymphoma 45 2004 965 969 10.1080/10428190310001638832 15291356

139 He J. Yang L. Meng X. Wei G. Wu W. Han X. Zheng G. Zheng W. Ye X. Shi J. Xie W. Zhang J. Huang H. Lin M. Cai Z. A retrospective analysis of cytogenetic and clinical characteristics in patients with multiple myeloma Am. J. Med. Sci. 345 2013 88 93 10.1097/MAJ.0b013e31825b32bc 22986611

140 Rack K. Vidrequin S. Dargent J.L. Genomic profiling of myeloma: the best approach, a comparison of cytogenetics, FISH and array-CGH of 112 myeloma cases J. Clin. Pathol. 69 2016 82 86 10.1136/jclinpath-2015-203054 26338801

141 Goldman-Mazur S. Vesole D.H. Jurczyszyn A. Clinical implications of cytogenetic and molecular aberrations in multiple myeloma Acta Haematol. Pol. 52 2021 18 28 10.5603/AHP.2021.0004

142 Su D. Zhang D. Chen K. Lu J. Wu J. Cao X. Ying L. Jin Q. Ye Y. Xie Z. Xiong L. Mao W. Li F. High performance of targeted next generation sequencing on variance detection in clinical tumor specimens in comparison with current conventional methods J. Experim. Clin. Cancer Res. 36 1 2017 121 10.1186/s13046-017-0591-4 201736- PMC5590190 28882180

143 Coffey D. Meerson M. Bardhan M. Lyubchenko E. Fernandes S. Kazandjian D. Hoffman J. Diamond B. Pandey A. Kaddoura M. Walker B. Mompoint S. Koubek K. Gutierrez S. Diaz C. Martinez Y. Shah F. Rosen S. Garcia W. Dominguez Y. Armogan M. Alesse J. Abdou M. Sheehan L. Shavronskaya D. Lupova D. Goldberg M. Landgren O. Replacing FISH with a comprehensive integrated approach for tumor genotyping and immune monitoring in multiple myeloma: the flagship study Blood 146 2025 7479 10.1182/blood-2025-7479 –7479

144 Yellapantula V. Hultcrantz M. Rustad E.H. Wasserman E. Londono D. Cimera R. Ciardiello A. Landau H. Akhlaghi T. Mailankody S. Patel M. Medina-Martinez J.S. Arango Ossa J.E. Levine M.F. Bolli N. Maura F. Dogan A. Papaemmanuil E. Zhang Y. Landgren O. Comprehensive detection of recurring genomic abnormalities: a targeted sequencing approach for multiple myeloma Blood Cancer J. 9 12 2019 101 10.1038/s41408-019-0264-y 20199- 31827071 PMC6906304

145 Giguère A. Raymond-Bouchard I. Collin V. Claveau J.S. Hébert J. LeBlanc R. Optical genome mapping reveals the complex genetic landscape of myeloma Cancers 15 2023 4687 10.3390/cancers15194687 37835381 PMC10571866

146 Ye J.C. Tang G. Pasvolsky O. Becnel M.R. Gaballa M.R. Patel K.K. Lee H.C. Thomas S.K. Weber D.M. Lin P. Orlowski R.Z. Unveiling clinical potential: exploring cytogenomic aberrations through optical genomic mapping in multiple myeloma Blood 144 2024 1971 10.1182/blood-2024-212346 –1971

147 Yoon J. Kwon J.A. Yoon S.Y. Clinical validation of optical genome mapping in multiple myeloma without plasma cell enrichment Am. J. Hematol. 100 2025 700 703 10.1002/ajh.27589 39821944

148 Puiggros A. Mallo M. Díaz-González Á. Avetisyan G. Cisneros A. Collado R. García-Hoyos M. García-Serra R. García-Ruiz C. Genescà E. Gómez-García A. González C. Ibáñez M. Kamaso J. Ramírez-Orellana M. Rodríguez-Rivera M. Salido M. Such E. Solé F. Álvarez-De Andrés S. Granada I. Espinet B. Optical genome mapping: technical basis and applications in hematological malignancies Revista Sangre 42 2023 083 098 10.24875/sangree.m23000013

149 Levy B. Burnside R.D. Akkari Y. Optical genome mapping: a new tool for cytogenomic analysis Genes 16 2025 924 10.3390/genes16080924 40869972 PMC12385997

150 Balasubramanian D. Pinto P.B. Grasso A. Vincent S. Tarayre H. Lajoignie D. Ghavi-Helm Y. Enhancer–promoter interactions can form independently of genomic distance and be functional across TAD boundaries Nucleic. Acids. Res. 52 2024 1702 1719 10.1093/NAR/GKAD1183 38084924 PMC10899756

151 Cardona-benavides I.J. de Ramón C. Gutiérrez N.C. Genetic abnormalities in multiple myeloma: prognostic and therapeutic implications Cells 10 2021 336 10.3390/CELLS10020336 33562668 PMC7914805

152 Dutta A.K. Alberge J.B. Sklavenitis-Pistofidis R. Lightbody E.D. Getz G. Ghobrial I.M. Single-cell profiling of tumour evolution in multiple myeloma — Opportunities for precision medicine Nat. Rev. Clin. Oncol. 19 4 2022 223 236 10.1038/s41571-021-00593-y 202219 35017721

153 Wu P. Li T. Li R. Jia L. Zhu P. Liu Y. Chen Q. Tang D. Yu Y. Li C. 3D genome of multiple myeloma reveals spatial genome disorganization associated with copy number variations Nat. Commun. 8 1 2017 1937 10.1038/s41467-017-01793-w 20178- 29203764 PMC5715138

154 Mikulasova A. Wardell C.P. Murison A. Boyle E.M. Jackson G.H. Smetana J. Kufova Z. Pour L. Sandecka V. Almasi M. Vsianska P. Gregora E. Kuglik P. Hajek R. Davies F.E. Morgan G.J. Walker B.A. The spectrum of somatic mutations in monoclonal gammopathy of undetermined significance indicates a less complex genomic landscape than that in multiple myeloma Haematologica 102 2017 1617 1625 10.3324/HAEMATOL.2017.163766 28550183 PMC5685224

155 Hoang P.H. Cornish A.J. Dobbins S.E. Kaiser M. Houlston R.S. Mutational processes contributing to the development of multiple myeloma Blood Cancer J. 9 8 2019 10.1038/s41408-019-0221-9 2019960- PMC6684612 31387987

156 Roth A. Khattra J. Yap D. Wan A. Laks E. Biele J. Ha G. Aparicio S. Bouchard-Côté A. Shah S.P. PyClone: statistical inference of clonal population structure in cancer Nat. Methods 11 2014 396 398 10.1038/NMETH.2883 24633410 PMC4864026

157 Bolli N. Avet-Loiseau H. Wedge D. Van Loo P. Alexandrov L. Martincorena I. Dawson K. Iorio F. Nik-Zainal S. Bignell G.R. Hinton J. Tubio J. McLaren S. Sarah O. Butler A. Jon T. Mudie L.J. Tai Y.-T. Shammas M. Sperling A. Fulciniti M. Richardson P.G. Magrangeas F. Minvielle S. Moreau P. Attal M. Futreal A. Facon T. Anderson K.C. Campbell P.J. Munshi N. Whole exome sequencing of multiple myeloma reveals an heterogeneous clonal architecture and genomic evolution Blood 122 2013 399 10.1182/BLOOD.V122.21.399.399 –399

158 Maura F. Bolli N. Angelopoulos N. Dawson K.J. Leongamornlert D. Martincorena I. Mitchell T.J. Fullam A. Gonzalez S. Szalat R. Abascal F. Rodriguez-Martin B. Samur M.K. Glodzik D. Roncador M. Fulciniti M. Tai Y.T. Minvielle S. Magrangeas F. Moreau P. Corradini P. Anderson K.C. Tubio J.M.C. Wedge D.C. Gerstung M. Avet-Loiseau H. Munshi N. Campbell P.J. Genomic landscape and chronological reconstruction of driver events in multiple myeloma Nat. Commun. 10 1 2019 10.1038/s41467-019-11680-1 2019103835- PMC6707220 31444325

159 Rashid N.U. Sperling A.S. Bolli N. Wedge D.C. Van Loo P. Tai Y.T. Shammas M.A. Fulciniti M. Samur M.K. Richardson P.G. Magrangeas F. Minvielle S. Futreal P.A. Anderson K.C. Avet-Loiseau H. Campbell P.J. Parmigiani G. Munshi N.C. Differential and limited expression of mutant alleles in multiple myeloma Blood 124 2014 3110 3117 10.1182/BLOOD-2014-04-569327 25237203 PMC4231420

160 Leich E. Weibach S. Klein H.U. Grieb T. Pischimarov J. Stuhmer T. Chatterjee M. Steinbrunn T. Langer C. Eilers M. Knop S. Einsele H. Bargou R. Rosenwald A. Multiple myeloma is affected by multiple and heterogeneous somatic mutations in adhesion- and receptor tyrosine kinase signaling molecules Blood Cancer J. 3 2 2013 e102–e102 10.1038/bcj.2012.47 20133 PMC3584721 23396385

161 Krzeminski P. Corchete L.A. García J.L. López-Corral L. Fermiñán E. García E.M. Martín A.A. Hernández-Rivas J.M. García-Sanz R. Miguel J.F.S. Gutiérrez N.C. Krzeminski P. Corchete L.A. García J.L. López-Corral L. Fermiñán E. García E.M. Martín A.A. Hernández-Rivas J.M. García-Sanz R. San Miguel J.F. Gutiérrez N.C. Integrative analysis of DNA copy number, DNA methylation and gene expression in multiple myeloma reveals alterations related to relapse Oncotarget. 7 2016 80664 80679 10.18632/ONCOTARGET.13025 27811368 PMC5348347

162 Martino Alessandro Sainz J. Buda G. Jamroziak K. Reis R.M. García-Sanz R. Jurado M. Ríos R. Szemraj-Rogucka Z. Marques H. Lesueur F. Moreno V. Orciuolo E. Gemignani F. Landi S. Rossi A.M. Dumontet C. Petrini M. Campa D. Canzian F. Genetics and molecular epidemiology of multiple myeloma: the rationale for the IMMEnSE consortium (review) Int. J. Oncol. 40 2012 625 638 10.3892/ijo.2011.1284 22159523

163 Erickson S.W. Raj V.R. Stephens O.W. Dhakal I. Chavan S.S. Sanathkumar N. Coleman E.A. Lee J.Y. Goodwin J.A. Apewokin S. Zhou D. Epstein J. Heuck C.J. Vangsted A.J. Genome-wide scan identifies variant in 2q12.3 associated with risk for multiple myeloma Blood 124 2014 2001 2003 10.1182/BLOOD-2014-07-586701 25237182

164 Du Z. Weinhold N. Song G.C. Rand K.A. Van Den Berg D.J. Hwang A.E. Sheng X. Hom V. Ailawadhi S. Nooka A.K. Singhal S. Pawlish K. Peters E.S. Bock C. Mohrbacher A. Stram A. Berndt S.I. Blot W.J. Casey G. Stevens V.L. Kittles R. Goodman P.J. Diver W.R. Hennis A. Nemesure B. Klein E.A. Rybicki B.A. Stanford J.L. Witte J.S. Signorello L. John E.M. Bernstein L. Stroup A.M. Stephens O.W. Zangari M. Van Rhee F. Olshan A. Zheng W. Hu J.J. Ziegler R. Nyante S.J. Ingles S.A. Press M.F. Carpten J.D. Chanock S.J. Mehta J. Colditz G.A. Wolf J. Martin T.G. Tomasson M. Fiala M.A. Terebelo H. Janakiraman N. Kolonel L. Anderson K.C. Le Marchand L. Auclair D. Chiu B.C.H. Ziv E. Stram D. Vij R. Bernal-Mizrachi L. Morgan G.J. Zonder J.A. Huff C.A. Lonial S. Orlowski R.Z. Conti D.V. Haiman C.A. Cozen W. A meta-analysis of genome-wide association studies of multiple myeloma among men and women of African ancestry Blood Adv. 4 2020 181 190 10.1182/BLOODADVANCES.2019000491 31935283 PMC6960456

165 Went M. Duran-Lozano L. Halldorsson G.H. Gunnell A. Ugidos-Damboriena N. Law P. Ekdahl L. Sud A. Thorleifsson G. Thodberg M. Olafsdottir T. Lamarca-Arrizabalaga A. Cafaro C. Niroula A. Ajore R. Lopez de Lapuente Portilla A. Ali Z. Pertesi M. Goldschmidt H. Stefansdottir L. Kristinsson S.Y. Stacey S.N. Love T.J. Rognvaldsson S. Hajek R. Vodicka P. Pettersson-Kymmer U. Späth F. Schinke C. Van Rhee F. Sulem P. Ferkingstad E. Hjorleifsson Eldjarn G. Mellqvist U.H. Jonsdottir I. Morgan G. Sonneveld P. Waage A. Weinhold N. Thomsen H. Försti A. Hansson M. Juul-Vangsted A. Thorsteinsdottir U. Hemminki K. Kaiser M. Rafnar T. Stefansson K. Houlston R. Nilsson B. Deciphering the genetics and mechanisms of predisposition to multiple myeloma Nat. Commun. 15 1 2024 6644 10.1038/s41467-024-50932-7 202415- 39103364 PMC11300596

166 Campa D. Martino A. Sainz J. Buda G. Jamroziak K. Weinhold N. Reis R.M.V. García-Sanz R. Jurado M. Ríos R. Szemraj-Rogucka Z. Marques H. Lesueur F. Bugert P. Moreno V. Szemraj J. Orciuolo E. Gemignani F. Rossi A.M. Dumontet C. Petrini M. Goldschmidt H. Landi S. Canzian F. Comprehensive investigation of genetic variation in the 8q24 region and multiple myeloma risk in the IMMEnSE consortium Br. J. Haematol. 157 2012 331 338 10.1111/j.1365-2141.2012.09047.x 22590720

167 Martino Alessandro Campa D. Jamroziak K. Reis R.M. Sainz J. Buda G. García-Sanz R. Lesueur F. Marques H. Moreno V. Jurado M. Ríos R. Szemraj-Rogucka Z. Szemraj J. Tjønneland A. Overvad K. Vangsted A.J. Vogel U. Mikala G. Kádár K. Szombath G. Varkonyi J. Orciuolo E. Dumontet C. Gemignani F. Rossi A.M. Landi S. Petrini M. Houlston R.S. Hemminki K. Canzian F. Impact of polymorphic variation at 7p15.3, 3p22.1 and 2p23.3 loci on risk of multiple myeloma Br. J. Haematol. 158 2012 805 809 10.1111/j.1365-2141.2012.09244.x 22823248

168 Martino A. Campa D. Jurczyszyn A. Martínez-López J. Moreno M.J. Varkonyi J. Dumontet C. García-Sanz R. Gemignani F. Jamroziak K. Stȩpień A. Hove Jacobsen S.E. Andersen V. Jurado M. Landi S. Rossi A.M. Lesueur F. Marques H. Dudziński M. Wa̧tek M. Moreno V. Orciuolo E. Petrini M. Reis R.M. Ríos R. Sainz J. Vogel U. Buda G. Vangsted A.J. Canzian F. Genetic variants and multiple myeloma risk: iMMEnSE validation of the best reported associations–an extensive replication of the associations from the candidate gene era Cancer Epidemiol. Biomarkers Prev. 23 2014 670 674 10.1158/1055-9965.EPI-13-1115 24521996

169 Clavero E. Sanchez-Maldonado J.M. Macauda A. Ter Horst R. Sampaio-Marques B. Jurczyszyn A. Clay-Gilmour A. Stein A. Hildebrandt M.A.T. Weinhold N. Buda G. García-Sanz R. Tomczak W. Vogel U. Jerez A. Zawirska D. Wątek M. Hofmann J.N. Landi S. Spinelli J.J. Butrym A. Kumar A. Martínez-López J. Galimberti S. Sarasquete M.E. Subocz E. Iskierka-Jażdżewska E. Giles G.G. Rybicka-Ramos M. Kruszewski M. Abildgaard N. Verdejo F.G. Sánchez Rovira P. da Silva Filho M.I. Kadar K. Razny M. Cozen W. Pelosini M. Jurado M. Bhatti P. Dudzinski M. Druzd-Sitek A. Orciuolo E. Li Y. Norman A.D. Zaucha J.M. Reis R.M. Markiewicz M. Rodríguez Sevilla J.J. Andersen V. Jamroziak K. Hemminki K. Berndt S.I. Rajkumar V. Mazur G. Kumar S.K. Ludovico P. Nagler A. Chanock S.J. Dumontet C. Machiela M.J. Varkonyi J. Camp N.J. Ziv E. Vangsted A.J. Brown E.E. Campa D. Vachon C.M. Netea M.G. Canzian F. Försti A. Sainz J. Polymorphisms within autophagy-related genes as susceptibility biomarkers for multiple myeloma: a meta-analysis of three large cohorts and functional characterization Int. J. Mol. Sci. 24 2023 8500 10.3390/ijms24108500 37239846 PMC10218542

170 Ríos-Tamayo R. Lupiañez C.B. Campa D. Hielscher T. Weinhold N. Martínez-López J. Jerez A. Landi S. Jamroziak K. Dumontet C. Watek M. Lesueur F. Reis R.M. Marques H. Jurczyszyn A. Vogel U. Buda G. García-Sanz R. Orciuolo E. Petrini M. Vangsted A.J. Gemignani F. Försti A. Goldschmidt H. Hemminki K. Canzian F. Jurado M. Sainz J. A common variant within the HNF1B gene is associated with overall survival of multiple myeloma patients: results from the IMMEnSE consortium and meta-analysis Oncotarget. 7 2016 59029 10.18632/oncotarget.10665 PMC5312293 27437873

171 Erčulj N. Kovač V. Hmeljak J. Dolžan V. The influence of platinum pathway polymorphisms on the outcome in patients with malignant mesothelioma Ann. Oncol. 23 2012 961 967 10.1093/annonc/mdr324 21765044

172 Dasgupta R.K. Adamson P.J. Davies F.E. Rollinson S. Roddam P.L. Ashcroft A.J. Dring A.M. Fenton J.A.L. Child J.A. Allan J.M. Morgan G.J. Polymorphic variation in GSTP1 modulates outcome following therapy for multiple myeloma Blood 102 2003 2345 2350 10.1182/BLOOD-2003-02-0444 12791655

173 Xiao Z. Yin G. Ni Y. Qu X. Wu H. Lu H. Qian S. Chen L. Li J. Qiu H. Miao K. MDR1 polymorphisms affect the outcome of Chinese multiple myeloma patients Biomedicine and Pharmacotherapy 95 2017 743 748 10.1016/j.biopha.2017.08.142 28888211

174 Macauda A. Castelli E. Buda G. Pelosini M. Butrym A. Watek M. Kruszewski M. Vangsted A.J. Rymko M. Jamroziak K. Abildgaard N. Haastrup E.K. Mazur G. Ríos R. Jurczyszyn A. Zawirska D. Dudziński M. Raźny M. Dutka M. Tomczak W. Suska A. Druzd-Sitek A. Marques H. Petrini M. Markiewicz M. Martinez-Lopez J. Ebbesen L.H. Iskierka-Jażdżewska E. Sainz J. Canzian F. Campa D. Inherited variation in the xenobiotic transporter pathway and survival of multiple myeloma patients Br. J. Haematol. 183 2018 375 384 10.1111/bjh.15521 30079960

175 Razi B. Anani Sarab G. Omidkhoda A. Alizadeh S. Multidrug resistance 1 (MDR1/ABCB1) gene polymorphism (rs1045642 C > T) and susceptibility to multiple myeloma: a systematic review and meta-analysis Hematology. 23 2018 456 462 10.1080/10245332.2018.1443897 29495954

176 Martino A. Campa D. Buda G. Sainz J. García-Sanz R. Jamroziak K. Reis R.M. Weinhold N. Jurado M. Ríos R. Szemraj-Rogucka Z. Marques H. Szemraj J. Stein A. Kumar R. Orciuolo E. Gemignani F. Landi S. Goldschmidt H. Petrini M. Dumontet C. Canzian F. Rossi A.M. Polymorphisms in xenobiotic transporters ABCB1, ABCG2, ABCC2, ABCC1, ABCC3 and multiple myeloma risk: a case-control study in the context of the International Multiple Myeloma rESEarch (IMMEnSE) consortium Leukemia 26 2012 1419 1422 10.1038/leu.2011.352 22182917

177 Iskierka-Jażdżewska E. Canzian F. Stępień A. Martino A. Campa D. Stein A. Krawczyk-Kuliś M. Rybicka-Ramos M. Kyrcz-Krzemień S. Butrym A. Mazur G. Jurczyszyn A. Zawirska D. Grząśko N. Tomczak W. Subocz E. Wątek M. Pasiarski M. Rymko M. Całbecka M. Druzd-Sitek A. Walewski J. Kruszewski M. Raźny M. Zaucha J.M. Dudziński M. Gaj P. Robak T. Warzocha K. Jamroziak K. Cereblon (CRBN) gene polymorphisms predict clinical response and progression-free survival in relapsed/refractory multiple myeloma patients treated with lenalidomide: a pharmacogenetic study from the IMMEnSE consortium Leuk. Lymphoma 61 2020 699 706 10.1080/10428194.2019.1689391 31746254

178 Iskierka-Jazdzewska E. Stepien A. Canzian F. Martino A. Campa D. Stein A. Krawczyk-Kulis M. Rybicka M. Kyrcz-Krzemien S. Butrym A.K. Mazur G. Jurczyszyn A.J. Zawirska D. Grzasko N. Tomczak W. Subocz E. Watek M. Pasiarski M. Rymko M. Calbecka M. Druzd-Sitek A. Walewski J. Kruszewski M. Razny M. Zaucha J.M. Dudzinski M. Gaj P. Warzocha K. Jamroziak K. Cereblon (CRBN) gene polymorphisms predict clinical response and progression-free survival in multiple myeloma patients treated with lenalidomide: a pharmacogenetic study of Immense Consortium Blood 124 2014 3628 10.1182/blood.v124.21.3628.3628 –3628 31746254

179 Szudy-Szczyrek A. Mlak R. Szczyrek M. Chocholska S. Sompor J. Nogalski A. Małecka-Massalska T. Hus M. Szudy-Szczyrek A. Mlak R. Szczyrek M. Chocholska S. Sompor J. Nogalski A. Małecka-Massalska T. Hus M. Polymorphisms in the promoter region of the CRBN gene as a predictive factor for the first-line CTD therapy in multiple myeloma patients Oncotarget. 9 2018 24054 24068 10.18632/oncotarget.25307 29844872 PMC5963627

180 Zhao X. Hua M. Yan S. Yu J. Han F. Zhong C. Wang R. Zhang C. Hou M. Ma D. The genetic polymorphisms of NLRP3 inflammasome associated with T helper cells in patients with multiple myeloma J. Immunol. Res. 2018 7569809 10.1155/2018/7569809 2018 PMC6126087 30211233

181 Shahzad M.N. Ijaz I. Naqvi S.S.Z.H. Yan C. Lin F. Li S. Huang C. Association between interleukin gene polymorphisms and multiple myeloma susceptibility Mol. Clin. Oncol. 12 2020 212 224 10.3892/mco.2020.1979 32064097 PMC7016519

182 Ali M. Ajore R. Wihlborg A.K. Niroula A. Swaminathan B. Johnsson E. Stephens O.W. Morgan G. Meissner T. Turesson I. Goldschmidt H. Mellqvist U.H. Gullberg U. Hansson M. Hemminki K. Nahi H. Waage A. Weinhold N. Nilsson B. The multiple myeloma risk allele at 5q15 lowers ELL2 expression and increases ribosomal gene expression Nat. Commun. 9 1 2018 10.1038/s41467-018-04082-2 201891649- PMC5917026 29695719

183 Swaminathan B. Thorleifsson G. Jöud M. Ali M. Johnsson E. Ajore R. Sulem P. Halvarsson B.M. Eyjolfsson G. Haraldsdottir V. Hultman C. Ingelsson E. Kristinsson S.Y. Kähler A.K. Lenhoff S. Masson G. Mellqvist U.H. Månsson R. Nelander S. Olafsson I. Sigurdardottir O. Steingrimsdóttir H. Vangsted A. Vogel U. Waage A. Nahi H. Gudbjartsson D.F. Rafnar T. Turesson I. Gullberg U. Stefánsson K. Hansson M. Thorsteinsdóttir U. Nilsson B. Variants in ELL2 influencing immunoglobulin levels associate with multiple myeloma Nat. Commun. 6 1 2015 7213 10.1038/ncomms8213 20156- 26007630 PMC4455110

184 Andersen N.F. Vogel U. Klausen T.W. Gimsing P. Gregersen H. Abildgaard N. Vangsted A.J. Vascular endothelial growth factor (VEGF) gene polymorphisms may influence the efficacy of thalidomide in multiple myeloma Int. J. Cancer 131 2012 E636–E642 10.1002/ijc.27387 22139971

185 Lopes-Aguiar L. Delamain M.T. Brito A.B.C. Lourenço G.J. Costa E.F.D. Oliveira G.B. Vassallo J. De Souza C.A. Lima C.S.P. VEGF, VEGFR2 and GSTM1 polymorphisms in outcome of multiple myeloma patients treated with thalidomide-based regimens Blood Cancer J. 7 2017 e580 10.1038/bcj.2017.58 PMC5520405 28665417

186 Campa D. Martino A. Macauda A. Dudziński M. Suska A. Druzd-Sitek A. Raab M.S. Moreno V. Huhn S. Butrym A. Sainz J. Szombath G. Rymko M. Marques H. Lesueur F. Vangsted A.J. Vogel U. Kruszewski M. Subocz E. Buda G. Iskierka-Jażdżewska E. Ríos R. Merz M. Schöttker B. Mazur G. Perrial E. Martinez-Lopez J. Butterbach K. García Sanz R. Goldschmidt H. Brenner H. Jamroziak K. Reis R.M. Kadar K. Dumontet C. Wątek M. Haastrup E.K. Helbig G. Jurczyszyn A. Jerez A. Varkonyi J. Barington T. Grzasko N. Zaucha J.M. Andersen V. Zawirska D. Canzian F. Genetic polymorphisms in genes of class switch recombination and multiple myeloma risk and survival: an IMMEnSE study Leuk. Lymphoma 60 2019 1803 1811 10.1080/10428194.2018.1551536 30633655

187 Beksac M. Gragert L. Fingerson S. Maiers M. Zhang M.J. Albrecht M. Zhong X. Cozen W. Dispenzieri A. Lonial S. Hari P. HLA polymorphism and risk of multiple myeloma Leukemia 30 2016 2260 2264 10.1038/leu.2016.199 27461064 PMC5134914

188 Beelen N.A. Molenbroeck S.J.J. Groeneveld L. Voorter C.E. Bos G.M.J. Wieten L. HLA class I NK-epitopes and KIR diversities in patients with multiple myeloma Immunogenetics 76 3 2024 155 164 10.1007/s00251-024-01336-w 202476 38478091 PMC11087314

189 Martínez-Sánchez M.V. Periago A. Legaz I. Gimeno L. Mrowiec A. Montes-Barqueros N.R. Campillo J.A. Bolarin J.M. Bernardo M.V. López-Álvarez M.R. González C. García-Garay M.C. Muro M. Cabañas-Perianes V. Fuster J.L. García-Alonso A.M. Moraleda J.M. Álvarez-Lopez M.R. Minguela A. Overexpression of KIR inhibitory ligands (HLA-I) determines that immunosurveillance of myeloma depends on diverse and strong NK cell licensing Oncoimmunology. 5 2016 10.1080/2162402X.2015.1093721 PMC4839374 27141379

190 Macauda A. Briem K. Clay-Gilmour A. Cozen W. Försti A. Giaccherini M. Corradi C. Sainz J. Niazi Y. ter Horst R. Li Y. Netea M.G. Vogel U. Hemminki K. Slager S.L. Varkonyi J. Andersen V. Iskierka-Jazdzewska E. Mártinez-Lopez J. Zaucha J. Camp N.J. Rajkumar S.V. Druzd-Sitek A. Bhatti P. Chanock S.J. Kumar S.K. Subocz E. Mazur G. Landi S. Machiela M.J. Jerez A. Norman A.D. Hildebrandt M.A.T. Kadar K. Berndt S.I. Ziv E. Buda G. Nagler A. Dumontet C. Raźny M. Watek M. Butrym A. Grzasko N. Dudzinski M. Rybicka-Ramos M. Matera E.L. García-Sanz R. Goldschmidt H. Jamroziak K. Jurczyszyn A. Clavero E. Giles G.G. Pelosini M. Zawirska D. Kruszewski M. Marques H. Haastrup E. Sánchez-Maldonado J.M. Bertsch U. Rymko M. Raab M.S. Brown E.E. Hofmann J.N. Vachon C. Campa D. Canzian F. Identification of novel genetic loci for risk of multiple myeloma by functional annotation Leukemia 37 11 2023 2326 2329 10.1038/s41375-023-02022-8 202337 37723249 PMC10624610

191 Macauda A. Piredda C. Clay-Gilmour A.I. Sainz J. Buda G. Markiewicz M. Barington T. Ziv E. Hildebrandt M.A.T. Belachew A.A. Varkonyi J. Prejzner W. Druzd-Sitek A. Spinelli J. Andersen N.F. Hofmann J.N. Dudziński M. Martinez-Lopez J. Iskierka-Jazdzewska E. Milne R.L. Mazur G. Giles G.G. Ebbesen L.H. Rymko M. Jamroziak K. Subocz E. Reis R.M. Garcia-Sanz R. Suska A. Haastrup E.K. Zawirska D. Grzasko N. Vangsted A.J. Dumontet C. Kruszewski M. Dutka M. Camp N.J. Waller R.G. Tomczak W. Pelosini M. Raźny M. Marques H. Abildgaard N. Wątek M. Jurczyszyn A. Brown E.E. Berndt S. Butrym A. Vachon C.M. Norman A.D. Slager S.L. Gemignani F. Canzian F. Campa D. Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients Int. J. Cancer 149 2021 327 336 10.1002/ijc.33547 33675538 PMC8770990

192 Melaiu O. Macauda A. Sainz J. Calvetti D. Facioni M.S. Maccari G. ter Horst R. Netea M.G. Li Y. Grząśko N. Moreno V. Jurczyszyn A. Jerez A. Watek M. Varkonyi J. Garcia-Sanz R. Kruszewski M. Dudziński M. Kadar K. Jacobsen S.E.H. Mazur G. Andersen V. Rybicka M. Zawirska D. Raźny M. Zaucha J.M. Ostrovsky O. Iskierka-Jazdzewska E. Reis R.M. Stępień A. Beider K. Nagler A. Druzd-Sitek A. Marques H. Martìnez-Lopez J. Lesueur F. Avet-Loiseau H. Vangsted A.J. Krawczyk-Kulis M. Butrym A. Jamroziak K. Dumontet C. Vogel U. Rymko M. Pelosini M. Subocz E. Szombath G. Sarasquete M.E. Silvestri R. Morani F. Landi S. Campa D. Canzian F. Gemignani F. Common gene variants within 3’-untranslated regions as modulators of multiple myeloma risk and survival Int. J. Cancer 148 2021 1887 1894 10.1002/ijc.33377 33152124

193 Fernández De Larrea C. Navarro A. Tejero R. Tovar N. Díaz T. Cibeira M.T. Rosinõl L. Ferrer G. Rovira M. Rozman M. Monzó M. Bladé J. Impact of MiRSNPs on survival and progression in patients with multiple myeloma undergoing autologous stem cell transplantation Clin. Cancer Res. 18 2012 3697 3704 10.1158/1078-0432.CCR-12-0191 22539802

194 Macauda A. Calvetti D. Maccari G. Hemminki K. Försti A. Goldschmidt H. Weinhold N. Houlston R. Andersen V. Vogel U. Buda G. Varkonyi J. Sureda A. Martinez Lopez J. Watek M. Butrym A. Sarasquete M.E. Dudziński M. Jurczyszyn A. Druzd-Sitek A. Kruszewski M. Subocz E. Petrini M. Iskierka-Jażdżewska E. Raźny M. Szombath G. Marques H. Zawirska D. Chraniuk D. Halka J. Hove Jacobsen S.E. Mazur G. García Sanz R. Dumontet C. Moreno V. Stępień A. Beider K. Pelosini M. Manuel Reis R. Krawczyk-Kulis M. Rymko M. Avet-Loiseau H. Lesueur F. Grząśko N. Ostrovsky O. Jamroziak K. Vangsted A.J. Jerez A. Tomczak W. Zaucha J.M. Kadar K. Sainz J. Nagler A. Landi S. Gemignani F. Canzian F. Identification of miRSNPs associated with the risk of multiple myeloma Int. J. Cancer 140 2017 526 534 10.1002/ijc.30465 27718532

195 Schilthuizen C. Broyl A. van der Holt B. de Knegt Y. Lokhorst H. Sonneveld P. van Oers M. Weijermans P. Fibbe W. Wittebol S. Schouten H. van Marwijk Kooy M. Biesma D. Baars J. Beverloo B. Westveer P. Steijaert M. van Rooyen K. van der Heiden I. van der Werf M. Medical Center, E Influence of genetic polymorphisms in CYP3A4, CYP3A5, GSTP1, GSTM1, GSTT1 and MDR1 genes on survival and therapy-related toxicity in multiple myeloma Haematologica 92 2007 277 278 10.3324/HAEMATOL.10618 17296590

196 Petrilla C. Galloway J. Kudalkar R. Ismael A. Cottini F. Understanding DNA damage response and DNA repair in multiple myeloma Cancers 15 2023 4155 10.3390/cancers15164155 37627183 PMC10453069

197 Saitoh T. Oda T. DNA damage response in multiple myeloma: the role of the tumor microenvironment Cancers 13 2021 504 10.3390/cancers13030504 33525741 PMC7865954

198 Kassambara A.A. Gourzones-Dmitriev C. Sahota S. Rème T. Moreaux J. Goldschmidt H. Constantinou A. Pasero P. Hose D. Klein B. A DNA repair pathway score predicts survival in human multiple myeloma: the potential for therapeutic strategy Oncotarget. 5 2014 2487 10.18632/oncotarget.1740 24809299 PMC4058021

199 Cea M. Cagnetta A. Adamia S. Acharya C. Tai Y.T. Fulciniti M. Ohguchi H. Munshi A. Acharya P. Bhasin M.K. Zhong L. Carrasco R. Monacelli F. Ballestrero A. Richardson P. Gobbi M. Lemoli R.M. Munshi N. Hideshima T. Nencioni A. Chauhan D. Anderson K.C. Evidence for a role of the histone deacetylase SIRT6 in DNA damage response of multiple myeloma cells Blood 127 2016 1138 1150 10.1182/blood-2015-06-649970 26675349 PMC4778164

200 Li Y. Li N. Yan Z. Li H. Chen L. Zhang Z. Fan G. Xu K. Li Z. Dysregulation of the NLRP3 inflammasome complex and related cytokines in patients with multiple myeloma Hematology. 21 2016 144 151 10.1179/1607845415Y.0000000029 26146985

201 Hofbauer D. Mougiakakos D. Broggini L. Zaiss M. Büttner-Herold M. Bach C. Spriewald B. Neumann F. Bisht S. Nolting J. Zeiser R. Hamarsheh S. Eberhardt M. Vera J. Visentin C. De Luca C.M.G. Moda F. Haskamp S. Flamann C. Böttcher M. Bitterer K. Völkl S. Mackensen A. Ricagno S. Bruns H. β2-microglobulin triggers NLRP3 inflammasome activation in tumor-associated macrophages to promote multiple myeloma progression Immunity 54 2021 1772 1787 10.1016/j.immuni.2021.07.002 e9 34289378

202 Park K.S. Bayles I. Szlachta-McGinn A. Paul J. Boiko J. Santos P. Liu J. Wang Z. Borghesi L. Milcarek C. Transcription elongation factor ELL2 drives ig secretory-specific mRNA production and the unfolded protein response J. Immunol. 193 2014 4663 4674 10.4049/jimmunol.1401608 25238757 PMC4201982

203 Rajkumar S.V. Kumar S. Multiple myeloma: diagnosis and treatment Mayo Clin. Proc. 91 2016 101 119 10.1016/j.mayocp.2015.11.007 26763514 PMC5223450

204 Brito A.B.C. Lourenço G.J. Oliveira G.B. De Souza C.A. Vassallo J. Lima C.S.P. Associations of VEGF and VEGFR2 polymorphisms with increased risk and aggressiveness of multiple myeloma Ann. Hematol. 93 8 2014 1363 1369 10.1007/s00277-014-2062-8 201493 24687381

205 Trowsdale J. Knight J.C. Major histocompatibility complex genomics and human disease Annu Rev. Genomics. Hum. Genet. 14 2013 301 323 10.1146/annurev-genom-091212-153455 23875801 PMC4426292

206 Mahaweni N.M. Ehlers F.A.I. Bos G.M.J. Wieten L. Tuning natural killer cell anti-multiple myeloma reactivity by targeting inhibitory signaling via KIR and NKG2A Front. Immunol. 9 2018 427229 10.3389/fimmu.2018.02848 PMC6288976 30564241

207 Nunes A.T. Annunziata C.M. Proteasome inhibitors: structure and function Semin. Oncol. 44 2018 377 10.1053/j.seminoncol.2018.01.004 PMC6020165 29935898

208 Besse A. Besse L. Kraus M. Mendez-Lopez M. Bader J. Xin B.T. de Bruin G. Maurits E. Overkleeft H.S. Driessen C. Proteasome inhibition in multiple myeloma: head-to-head comparison of currently available Proteasome inhibitors Cell Chem. Biol. 26 2019 340 351 10.1016/j.chembiol.2018.11.007 e3 30612952

209 Gandolfi S. Laubach J.P. Hideshima T. Chauhan D. Anderson K.C. Richardson P.G. The proteasome and proteasome inhibitors in multiple myeloma Cancer Metastasis Rev. 36 4 2017 561 584 10.1007/s10555-017-9707-8 201736 29196868

210 Mitra A.K. Harding T. Mukherjee U.K. Jang J.S. Li Y. Hongzheng R. Jen J. Sonneveld P. Kumar S. Kuehl W.M. Rajkumar V. Van Ness B. A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma Blood Cancer J. 7 6 2017 e581–e581 10.1038/BCJ.2017.56 20177 PMC5520403 28665416

211 Wang L. Kumar S. Fridley B.L. Kalari K.R. Moon I. Pelleymounter L.L. Hildebrandt M.A.T. Batzler A. Eckloff B.W. Wieben E.D. Greipp P.R. Proteasome β subunit pharmacogenomics: gene resequencing and functional genomics Clin. Cancer Res. 14 2008 3503 3513 10.1158/1078-0432.CCR-07-5150 18519783 PMC2778274

212 Ling S.C.W. Lau E.K.K. Al-Shabeeb A. Nikolic A. Catalano A. Iland H. Horvath N. Ho P.J. Harrison S. Fleming S. Joshua D.E. Allen J.D. Response of myeloma to the proteasome inhibitor bortezomib is correlated with the unfolded protein response regulator XBP-1 Haematologica 97 2012 64 72 10.3324/haematol.2011.043331 21993678 PMC3248932

213 Ri M. Endoplasmic-reticulum stress pathway-associated mechanisms of action of proteasome inhibitors in multiple myeloma Int. J. Hematol. 104 3 2016 273 280 10.1007/s12185-016-2016-0 2016104 27169614

214 Hideshima T. Ikeda H. Chauhan D. Okawa Y. Raje N. Podar K. Mitsiades C. Munshi N.C. Richardson P.G. Carrasco R.D. Anderson K.C. Bortezomib induces canonical nuclear factor-kappaB activation in multiple myeloma cells Blood 114 2009 1046 1052 10.1182/blood-2009-01-199604 19436050 PMC2721785

215 Sood N. Hamide A. Dubashi B. Jayanthi M. Munirajan A.K. Sood N. Hamide A. Dubashi B. Jayanthi M. Munirajan A. Influence of PSMB6 and PSMB9 genetic polymorphisms on bortezomib-based therapy response in newly diagnosed multiple myeloma Cureus. 17 2025 10.7759/cureus.81081 PMC12017296 40271316

216 Sedlacek J. Impact of proteostasis workload on sensitivity to proteasome inhibitors in multiple myeloma Clin. Exp. Med. 25 1 2025 176 10.1007/s10238-025-01713-z 202525- 40418254 PMC12106538

217 Kortüm K.M. Mai E.K. Hanafiah N.H. Shi C.X. Zhu Y.X. Bruins L. Barrio S. Jedlowski P. Merz M. Xu J. Stewart R.A. Andrulis M. Jauch A. Hillengass J. Goldschmidt H. Bergsagel P.L. Braggio E. Stewart A.K. Raab M.S. Targeted sequencing of refractory myeloma reveals a high incidence of mutations in CRBN and Ras pathway genes Blood 128 2016 1226 1233 10.1182/blood-2016-02-698092 27458004 PMC5524534

218 Vlayen S. Caruso M. De Keersmaecker K. Daelemans D. Delforge M. 3208 – Unravelling drug resistance and sensitization to proteasome inhibitors using genome-wide Crispr-Cas9 knockout screening Exp. Hematol. 124 2023 10.1016/j.exphem.2023.06.315 S153

219 Jayaweera S.P.E. Wanigasinghe Kanakanamge S.P. Rajalingam D. Silva G.N. Carfilzomib: a promising proteasome inhibitor for the treatment of relapsed and refractory multiple myeloma Front. Oncol. 11 2021 740796 10.3389/fonc.2021.740796 PMC8631731 34858819

220 Sissung T.M. Peer C.J. Korde N. Mailankody S. Kazandjian D. Venzon D.J. Landgren O. Figg W.D. Carfilzomib and lenalidomide response related to VEGF and VEGFR2 germline polymorphisms Cancer Chemother Pharmacol. 80 1 2017 217 221 10.1007/s00280-017-3323-8 201780 28488026 PMC6387687

221 Muz B. Ghazarian R.N. Ou M. Luderer M.J. Kusdono H.D. Azab A.K. Spotlight on ixazomib: potential in the treatment of multiple myeloma Drug Des. Devel. Ther. 10 2016 217 226 10.2147/DDDT.S93602 PMC4714737 26811670

222 Ravi D. Beheshti A. Abermil N. Passero F. Sharma J. Coyle M. Kritharis A. Kandela I. Hlatky L. Sitkovsky M.V. Mazar A. Gartenhaus R.B. Evens A.M. Proteasomal inhibition by Ixazomib induces CHK1 and MYC dependent cell death in T-cell and Hodgkin lymphoma Cancer Res. 76 2016 3319 10.1158/0008-5472.CAN-15-2477 26988986 PMC5315412

223 Boonstra P.S. Polk A. Brown N. Hristov A.C. Bailey N.G. Kaminski M.S. Phillips T. Devata S. Mayer T. Wilcox R.A. A single center Phase II study of Ixazomib in patients with relapsed or refractory cutaneous or peripheral T-cell lymphomas Am. J. Hematol. 92 2017 1287 10.1002/ajh.24895 28842936 PMC6116510

224 Dash A.B. Zhang J. Shen L. Li B. Berg D. Lin J. Avet-Loiseau H. Bahlis N.J. Moreau P. Richardson P.G. Di Bacco A. Clinical benefit of ixazomib plus lenalidomide-dexamethasone in myeloma patients with non-canonical NF-κb pathway activation Eur. J. Haematol. 105 2020 274 10.1111/ejh.13435 32350909 PMC7497066

225 Moreau P. Masszi T. Grzasko N. Bahlis N.J. Hansson M. Pour L. Sandhu I. Ganly P. Baker B.W. Jackson S.R. Stoppa A.-M. Simpson D.R. Gimsing P. Palumbo A. Garderet L. Cavo M. Kumar S. Touzeau C. Buadi F.K. Laubach J.P. Berg D.T. Lin J. Di Bacco A. Hui A.-M. van de Velde H. Richardson P.G. Oral ixazomib, lenalidomide, and dexamethasone for multiple myeloma New Engl. J. Med. 374 2016 1621 1634 10.1056/nejmoa1516282 27119237

226 Sonneveld P. Avet-Loiseau H. Lonial S. Usmani S. Siegel D. Anderson K.C. Chng W.J. Moreau P. Attal M. Kyle R.A. Caers J. Hillengass J. Miguel J.S. Van De Donk N.W.C.J. Einsele H. Bladé J. Durie B.G.M. Goldschmidt H. Mateos M.V. Palumbo A. Orlowski R. Treatment of multiple myeloma with high-risk cytogenetics: a consensus of the International Myeloma Working Group Blood 127 2016 2955 10.1182/blood-2016-01-631200 27002115 PMC4920674

227 Avet-Loiseau H. Bahlis N.J. Chng W.J. Masszi T. Viterbo L. Pour L. Ganly P. Palumbo A. Cavo M. Langer C. Pluta A. Nagler A. Kumar S. Ben-Yehuda D. Rajkumar S.V. San-Miguel J. Berg D. Lin J. Van De Velde H. Esseltine D.L. di Bacco A. Moreau P. Richardson P.G. Ixazomib significantly prolongs progression-free survival in high-risk relapsed/refractory myeloma patients Blood 130 2017 2610 2618 10.1182/blood-2017-06-791228 29054911

228 Chang X. Zhu Y. Shi C. Stewart A.K. Mechanism of immunomodulatory drugs’ action in the treatment of multiple myeloma Acta Biochim. Biophys. Sin. (Shanghai) 46 2014 240 253 10.1093/abbs/gmt142 24374776 PMC4357794

229 Abe Y. Ishida T. Immunomodulatory drugs in the treatment of multiple myeloma Jpn. J. Clin. Oncol. 49 2019 695 702 10.1093/jjco/hyz083 31187860

230 Quach H. Ritchie D. Stewart A.K. Neeson P. Harrison S. Smyth M.J. Prince H.M. Mechanism of action of immunomodulatory drugs (IMiDS) in multiple myeloma Leukemia 24 2010 22 32 10.1038/leu.2009.236 19907437 PMC3922408

231 Barrio S. Munawar U. Zhu Y.X. Giesen N. Shi C.X. Da Viá M. Sanchez R. Bruins L. Demler T. Müller N. Haertle L. Garitano A. Steinbrunn T. Danhof S. Cuenca I. Barrio-Garcia C. Braggio E. Rosenwald A. Martinez-Lopez J. Rasche L. Raab M.S. Stewart A.K. Einsele H. Stühmer T. Kortüm K.M. IKZF1/3 and CRL4CRBN E3 ubiquitin ligase mutations and resistance to immunomodulatory drugs in multiple myeloma Haematologica 105 2020 e237 10.3324/HAEMATOL.2019.217943 PMC7193510 31558666

232 Gooding S. Ansari-Pour N. Towfic F. Estévez M.O. Chamberlain P.P. Tsai K.T. Flynt E. Hirst M. Rozelle D. Dhiman P. Neri P. Ramasamy K. Bahlis N. Vyas P. Thakurta A. Multiple Cereblon genetic changes associate with acquired resistance to Lenalidomide or Pomalidomide in Multiple Myeloma Blood 137 2020 232 237 10.1182/blood.2020007081 PMC7893409 33443552

233 Haertle L. Barrio S. Munawar U. Han S. Zhou X. Simicek M. Vogt C. Truger M. Fernandez R.A. Steinhardt M. Weingart J. Snaurova R. Nerreter S. Teufel E. Garitano-Trojaola A. Viá M.Da Ruiz-Heredia Y. Rosenwald A. Bolli N. Hajek R. Raab P. Raab M.S. Weinhold N. Haferlach C. Haaf T. Martinez-Lopez J. Einsele H. Rasche L. Kortüm K.M. Single-nucleotide variants and epimutations induce proteasome inhibitor resistance in multiple myeloma Clin. Cancer Res. 29 2023 279 288 10.1158/1078-0432.CCR-22-1161 36282272

234 Botta C. Mendicino F. Martino E.A. Vigna E. Ronchetti D. Correale P. Morabito F. Neri A. Gentile M. Mechanisms of immune evasion in multiple myeloma: open questions and therapeutic opportunities Cancers 2021 13 10.3390/cancers13133213 34203150 PMC8268448

235 Romano A. Conticello C. Cavalli M. Vetro C. La Fauci A. Parrinello N.L. Di Raimondo F. Immunological dysregulation in multiple myeloma microenvironment Biomed. Res. Int. 2014 198539 10.1155/2014/198539 2014 PMC4071780 25013764

236 Bagratuni T. Terpos E. Eleutherakis-Papaiakovou E. Kalapanida D. Gavriatopoulou M. Migkou M. Liacos C.I. Tasidou A. Matsouka C. Mparmparousi D. Dimopoulos M.A. Kastritis E. TLR4/TIRAP polymorphisms are associated with progression and survival of patients with symptomatic myeloma Br. J. Haematol. 172 2016 44 47 10.1111/bjh.13786 26564000

237 Huang P.A. Beedie S.L. Chau C.H. Venzon D.J. Gere S. Kazandjian D. Korde N. Mailankody S. Landgren O. Figg W.D. Cereblon gene variants and clinical outcome in multiple myeloma patients treated with lenalidomide Sci. Rep. 9 1 2019 10.1038/s41598-019-51446-9 2019914884- PMC6795854 31619706

238 Cohen A.D. Raje N. Fowler J.A. Mezzi K. Scott E.C. Dhodapkar M.V. How to train your T cells: overcoming immune dysfunction in multiple myeloma a C Clin. Cancer Res. 26 2020 1541 1554 10.1158/1078-0432.CCR-19-2111 31672768 PMC8176627

239 Dunn G.P. Old L.J. Schreiber R.D. The three Es of cancer immunoediting Annu. Rev. Immunol. 22 2004 329 360 10.1146/annurev.immunol.22.012703.104803 15032581

240 Krejcik J. Casneuf T. Nijhof I.S. Verbist B. Bald J. Plesner T. Syed K. Liu K. Van De Donk N.W.C.J. Weiss B.M. Ahmadi T. Lokhorst H.M. Mutis T. Sasser A.K. Daratumumab depletes CD38+ immune regulatory cells, promotes T-cell expansion, and skews T-cell repertoire in multiple myeloma Blood 128 2016 384 394 10.1182/blood-2015-12-687749 27222480 PMC4957162

241 Marone, R., Asllanaj, E., Capoferri, G., Schwede, T., Jeker, L.T., Lepore, R., 2025. Impact of Human genetic variation underlying resistance to antigen-specific immunotherapy. bioRxiv 2025.05.02.651174. 10.1101/2025.05.02.651174 .

242 Shah A. Rauth S. Aithal A. Kaur S. Ganguly K. Orzechowski C. Varshney G.C. Jain M. Batra S.K. The current landscape of antibody-based therapies in solid malignancies Theranostics. 11 2021 1493 1512 10.7150/thno.52614 33391547 PMC7738893

243 van de Donk N.W.C.J. Chari A. Mateos M.V. Mechanisms of resistance against T-cell engaging bispecific antibodies in multiple myeloma: implications for novel treatment strategies Lancet Haematol. 11 2024 e693–e707 10.1016/S2352-3026(24)00186-8 39033769

244 Qin X. Ning W. Liu H. Liu X. Luo W. Xia N. Stepping forward: t-cell redirecting bispecific antibodies in cancer therapy Acta Pharm. Sin. B 14 2024 2361 10.1016/j.apsb.2024.03.027 38828136 PMC11143529

245 Samur M.K. Fulciniti M. Aktas Samur A. Bazarbachi A.H. Tai Y.T. Prabhala R. Alonso A. Sperling A.S. Campbell T. Petrocca F. Hege K. Kaiser S. Loiseau H.A. Anderson K.C. Munshi N.C. Biallelic loss of BCMA as a resistance mechanism to CAR T cell therapy in a patient with multiple myeloma Nat. Commun. 12 1 2021 868 10.1038/s41467-021-21177-5 202112- 33558511 PMC7870932

246 Lee H. Ahn S. Maity R. Leblay N. Ziccheddu B. Truger M. Chojnacka M. Cirrincione A. Durante M. Tilmont R. Barakat E. Poorebrahim M. Sinha S. McIntyre J. Chan M.Y. Wilson A. Kyman H. Krishnan S. Landgren A. Walter O. Meggendorfer W. Haferlach M. Haferlach C. Einsele T. Kortüm H. Knop M.K. Alberge S. Rosenwald J.B. Keats A. Rasche J.J. Maura L. Neri F. Bahlis P. Mechanisms of antigen escape from BCMA- or GPRC5D-targeted immunotherapies in multiple myeloma Nat. Med. 29 9 2023 2295 2306 10.1038/s41591-023-02491-5 202329 37653344 PMC10504087

247 Firestone R.S. Socci N.D. Shekarkhand T. Zhu M. Qin W.G. Hultcrantz M. Mailankody S. Tan C.R. Korde N. Lesokhin A.M. Hassoun H. Shah U. Maclachlan K.H. Rajeeve S. Landau H.J. Scordo M. Shah G.L. Lahoud O.B. Giralt S. Murata K. Usmani S.Z. Chung D.J. Antigen escape as a shared mechanism of resistance to BCMA-directed therapies in multiple myeloma Blood 144 2024 402 407 10.1182/blood.2023023557 38728378 PMC11302451

248 Papadimitriou M. Ahn S. Diamond B.T. Lee H. McIntyre J.B. Truger M. Durante M.A. Ziccheddu B. Osz K. Landgren O. Rasche L. Bahlis N.J. Neri P. Maura F. Timing genomic antigen loss in multiple myeloma treated with T cell-redirecting immunotherapies Blood Cancer Discov. 6 2025 572 579 10.1158/2643-3230.BCD-25-0005 40906971 PMC12514755

249 Tedder B. Bhutani M. Resistance mechanisms to BCMA targeting bispecific antibodies and CAR T-cell therapies in multiple myeloma Cells 14 2025 1077 10.3390/cells14141077 2025Page141077 40710330 PMC12293154

250 Strohl W.R. Naso M. Bispecific T-cell redirection versus chimeric antigen receptor (CAR)-T cells as approaches to kill cancer cells Antibodies 8 2019 41 10.3390/antib8030041 31544847 PMC6784091

251 Lin H. Yang X. Ye S. Huang L. Mu W. Lin H. Yang X. Ye S. Huang L. Mu W. Huang L. Huang L. Antigen escape in CAR-T cell therapy: mechanisms and overcoming strategies Biomedicine & Pharmacotherapy 178 2024 117252 10.1016/J.BIOPHA.2024.117252 39098176

252 Majzner R.G. Mackall C.L. Tumor antigen escape from car t-cell therapy Cancer Discov. 8 2018 1219 1226 10.1158/2159-8290.CD-18-0442/333744/P/TUMOR-ANTIGEN-ESCAPE-FROM-CAR-T-CELL 30135176

253 Russell B.M. Avigan D.E. Immune dysregulation in multiple myeloma: the current and future role of cell-based immunotherapy Int. J. Hematol. 117 2023 652 659 10.1007/s12185-023-03579-x 36964840 PMC10039687

254 Visram A. Kourelis T.V. Aging-associated immune system changes in multiple myeloma: the dark side of the moon Cancer Treat. Res. Commun. 29 2021 10.1016/j.ctarc.2021.100494 PMC8744501 34837796

255 Jamal-Hanjani M. Thanopoulou E. Peggs K.S. Quezada S.A. Swanton C. Tumour heterogeneity and immune-modulation Curr. Opin. Pharmacol. 13 2013 497 503 10.1016/j.coph.2013.04.006 23664091 PMC3988963

256 Reuben A. Spencer C.N. Prieto P.A. Gopalakrishnan V. Reddy S.M. Miller J.P. Mao X. De Macedo M.P. Chen J. Song X. Jiang H. Chen P.L. Beird H.C. Garber H.R. Roh W. Wani K. Chen E. Haymaker C. Forget M.A. Little L.D. Gumbs C. Thornton R.L. Hudgens C.W. Chen W.S. Austin-Breneman J. Sloane R.S. Nezi L. Cogdill A.P. Bernatchez C. Roszik J. Hwu P. Woodman S.E. Chin L. Tawbi H. Davies M.A. Gershenwald J.E. Amaria R.N. Glitza I.C. Diab A. Patel S.P. Hu J. Lee J.E. Grimm E.A. Tetzlaff M.T. Lazar A.J. Wistuba I.I. Clise-Dwyer K. Carter B.W. Zhang J. Futreal P.A. Sharma P. Allison J.P. Cooper Z.A. Wargo J.A. Genomic and immune heterogeneity are associated with differential responses to therapy in melanoma NPJ. Genom. Med. 2 1 2017 10 10.1038/s41525-017-0013-8 20172- 28819565 PMC5557036

257 Kockx M.M. McCleland M. Koeppen H. Microenvironmental regulation of tumour immunity and response to immunotherapy J. Pathol. 254 2021 374 383 10.1002/path.5681 33846997 PMC8252752

258 Baldwin L.A. Bartonicek N. Yang J. Wu S.Z. Deng N. Roden D.L. Chan C.L. Al-Eryani G. Zanker D.J. Parker B.S. Swarbrick A. Junankar S. DNA barcoding reveals ongoing immunoediting of clonal cancer populations during metastatic progression and immunotherapy response Nat. Commun. 13 2022 6539 10.1038/s41467-022-34041-x 36344500 PMC9640547

259 Cao Q. Wu X. Zhang Q. Gong J. Chen Y. You Y. Shen J. Qiang Y. Cao G. Mechanisms of action of the BCL-2 inhibitor venetoclax in multiple myeloma: a literature review Front. Pharmacol. 14 2023 1291920 10.3389/fphar.2023.1291920 PMC10657905 38026941

260 Gupta V.A. Barwick B.G. Matulis S.M. Shirasaki R. Jaye D.L. Keats J.J. Oberlton B. Joseph N.S. Hofmeister C.C. Heffner L.T. Dhodapkar M.V. Nooka A.K. Lonial S. Mitsiades C.S. Kaufman J.L. Boise L.H. Venetoclax sensitivity in multiple myeloma is associated with B-cell gene expression Blood 137 2021 3604 3615 10.1182/blood.2020007899 33649772 PMC8462405

261 Leow C.C.Y. Low M.S.Y. Targeted therapies for multiple myeloma J. Pers. Med. 11 2021 334 10.3390/jpm11050334 33922567 PMC8145732

262 Naymagon L. Abdul-Hay M. Novel agents in the treatment of multiple myeloma: a review about the future J. Hematol. Oncol. 9 2016 10.1186/s13045-016-0282-1 PMC4929712 27363832

263 Egan P. Drain S. Conway C. Bjourson A.J. Alexander H.D. Towards stratified medicine in plasma cell myeloma Int. J. Mol. Sci. 17 2016 1760 10.3390/ijms17101760 2016Page171760 27775669 PMC5085784

264 Engelhardt M. Kortüm K.M. Goldschmidt H. Merz M. Functional cure and long-term survival in multiple myeloma: how to challenge the previously impossible Haematologica 109 2024 2420 10.3324/haematol.2023.283058 38356448 PMC11290544

265 Maura F. Rajanna A.R. Ziccheddu B. Poos A.M. Derkach A. MacLachlan K. Durante M. Diamond B. Papadimitriou M. Davies F. Boyle E.M. Walker B. Hultcrantz M. Silva A. Hampton O. Teer J.K. Siegel E.M. Bolli N. Jackson G.H. Kaiser M. Pawlyn C. Cook G. Kazandjian D. Stein C. Chesi M. Bergsagel L. Mai E.K. Goldschmidt H. Weisel K.C. Fenk R. Raab M.S. Van Rhee F. Usmani S. Shain K.H. Weinhold N. Morgan G. Landgren O. Genomic classification and individualized prognosis in multiple myeloma J. Clin. Oncol. 42 2024 1229 1240 10.1200/JCO.23.01277 38194610 PMC11095887

266 Ríos-Tamayo R. Sáinz J. Martínez-López J. Puerta J.M. Chang D.Y.L. Rodríguez T. Garrido P. de Veas J.L.G. Romero A. Moratalla L. López-Fernández E. González P.A. Sánchez M.J. Jiménez-Moleón J.J. Jurado M. Lahuerta J.J. Early mortality in multiple myeloma: the time-dependent impact of comorbidity: a population-based study in 621 real-life patients Am. J. Hematol. 91 2016 700 704 10.1002/ajh.24389 27074204

267 Aksenova A.Y. Zhuk A.S. Lada A.G. Zotova I.V. Stepchenkova E.I. Kostroma I.I. Gritsaev S.V. Pavlov Y.I. Genome instability in multiple myeloma: facts and factors Cancers 2021 13 10.3390/cancers13235949 34885058 PMC8656811

268 Kazandjian D. Mo C.C. Landgren O. Richardson P.G. The role of high-dose melphalan with autologous stem-cell transplant in multiple myeloma: is it time for a paradigm shift? Br. J. Haematol. 191 2020 692 703 10.1111/bjh.16764 32501533 PMC8505046

269 Nath C.E. Trotman J. Tiley C. Presgrave P. Joshua D. Kerridge I. Kwan Y.L. Gurney H. McLachlan A.J. Earl J.W. Nivison-Smith I. Zeng L. Shaw P.J. High melphalan exposure is associated with improved overall survival in myeloma patients receiving high dose melphalan and autologous transplantation Br. J. Clin. Pharmacol. 82 2016 149 159 10.1111/bcp.12906 26879446 PMC4917807

270 Bruins W.S.C. Zweegman S. Mutis T. van de Donk N.W.C.J. Targeted therapy with immunoconjugates for multiple myeloma Front. Immunol. 11 2020 10.3389/fimmu.2020.01155 PMC7316960 32636838

271 Thomas F. McLeod H. Watters J. Pharmacogenomics: the influence of genomic variation on drug response Curr. Top. Med. Chem. 4 2004 1397 1407 10.2174/1568026043387638 15379653

272 Long B. McCurdy A. Koyfman A. Rosenberg H. An emergency medicine review: multiple myeloma and its complications Am. J. Emergency Med. 88 2025 172 179 10.1016/j.ajem.2024.11.073 39643958

273 Palumbo A. Rajkumar S.V. San Miguel J.F. Larocca A. Niesvizky R. Morgan G. Landgren O. Hajek R. Einsele H. Anderson K.C. Dimopoulos M.A. Richardson P.G. Cavo M. Spencer A. Stewart A.K. Shimizu K. Lonial S. Sonneveld P. Durie B.G.M. Moreau P. Orlowski R.Z. International Myeloma Working Group consensus statement for the management, treatment, and supportive care of patients with myeloma not eligible for standard autologous stem-cell transplantation J. Clin. Oncol. 32 2014 587 600 10.1200/JCO.2013.48.7934 24419113 PMC3918540

274 Singhal D. Kutyna M.M. Hahn C.N. Shah M.V. Hiwase D.K. Therapy-related myeloid neoplasms: complex interactions among cytotoxic therapies, genetic factors, and aberrant microenvironment Blood Cancer Discov. 5 2024 400 416 10.1158/2643-3230.BCD-24-0103 39422544 PMC11528189

275 Campo C. Da Silva Filho M.I. Weinhold N. Goldschmidt H. Hemminki K. Merz M. Försti A. Genetic susceptibility to Bortezomib-induced peripheral neuroropathy: replication of the reported candidate susceptibility loci Neurochem. Res. 42 2017 925 931 10.1007/s11064-016-2007-9 27422265

276 Argyriou A.A. Iconomou G. Kalofonos H.P. Bortezomib-induced peripheral neuropathy in multiple myeloma: a comprehensive review of the literature Blood 112 2008 1593 1599 10.1182/blood-2008-04-149385 18574024

277 Magrangeas F. Kuiper R. Avet-Loiseau H. Gouraud W. Guerin-Charbonnel C. Ferrer L. Aussem A. Elghazel H. Suhard J. Sakissian H.Der Attal M. Munshi N.C. Sonneveld P. Dumontet C. Moreau P. Van Duin M. Campion L. Minvielle S. A genome-wide association study identifies a novel locus for Bortezomib-induced peripheral neuropathy in European patients with multiple myeloma Clin. Cancer Res. 22 2016 4350 4355 10.1158/1078-0432.CCR-15-3163 27060151 PMC5010494

278 Johnson D.C. Corthals S.L. Walker B.A. Ross F.M. Gregory W.M. Dickens N.J. Lokhorst H.M. Goldschmidt H. Davies F.E. Durie B.G.M. Van Ness B. Child J.A. Sonneveld P. Morgan G.J. Genetic factors underlying the risk of thalidomide-related neuropathy in patients with multiple myeloma J. Clin. Oncol. 29 2011 797 804 10.1200/JCO.2010.28.0792 21245421

279 Bernstein Z.S. Kim E.B. Raje N. Bone disease in multiple myeloma: biologic and clinical implications Cells 2022 11 10.3390/cells11152308 35954151 PMC9367243

280 Harada T. Hiasa M. Teramachi J. Abe M. Myeloma-bone interaction: a vicious cycle via TAK1-PIM2 signaling Cancers 2021 13 10.3390/cancers13174441 34503251 PMC8431187

281 Panaroni C. Yee A.J. Raje N.S. Myeloma and bone disease Curr. Osteoporos. Rep. 15 2017 483 498 10.1007/s11914-017-0397-5 28861842

282 Ru J.ying Wang Y.fen Osteocyte apoptosis: the roles and key molecular mechanisms in resorption-related bone diseases Cell Death. Dis. 11 2020 10.1038/s41419-020-03059-8 PMC7552426 33046704

283 Terpos E. Christoulas D. Gavriatopoulou M. Biology and treatment of myeloma related bone disease Metabolism. 80 2018 80 90 10.1016/j.metabol.2017.11.012 29175022

284 Johnson D.C. Weinhold N. Mitchell J. Chen B. Stephens O.W. Försti A. Nickel J. Kaiser M. Gregory W.A. Cairns D. Jackson G.H. Hoffmann P. Noethen M.M. Hillengass J. Bertsch U. Barlogie B. Davis F.E. Hemminki K. Goldschmidt H. Houlston R.S. Morgan G.J. Genetic factors influencing the risk of multiple myeloma bone disease Leukemia 30 4 2015 883 888 10.1038/leu.2015.342 201630 26669972 PMC4832071

285 Bustoros M. Sklavenitis-Pistofidis R. Park J. Redd R. Zhitomirsky B. Stewart C. Tai Y.-T. Barbera A. Aguet F. Mouhieddine T. Chavada S. Soong D. Boehner C. Neuse C. Dunford A. Rahmat M. Glen A.P. Van Allen E. Kumar S. Kastritis E. Morgan E. Laubach J. Dimopoulos M.A. Chiu C. Richardson P.G. Anderson K.C. Munshi N. Yong K. Trippa L. Ghobrial I. Manier S. Getz G. Genomic profiling of smoldering multiple myeloma identifies patients at a high risk of disease progression Clin. Lymphoma Myeloma Leuk. 19 2019 e5–e6 10.1016/j.clml.2019.09.007 PMC7367550 32442065

286 Giglia J.L. White M.J. Hart A.J. Toro J.J. Freytes C.O. Holt C.C. Cai Y. Williams S.M. Brandt S.J. A single nucleotide polymorphism in SLC7A5 is associated with gastrointestinal toxicity after high-dose melphalan and autologous stem cell transplantation for multiple myeloma Biol. Blood Marrow TransPlant 20 2014 1014 1020 10.1016/j.bbmt.2014.03.022 24704384 PMC4076151

287 Gleason C. Nooka A. Lonial S. Supportive therapies in multiple myeloma J. Natl. Compr. Canc. Netw. 7 2009 971 979 10.6004/jnccn.2009.0064 19878640

288 Coleman E.A. Lee J.Y. Erickson S.W. Goodwin J.A. Sanathkumar N. Raj V.R. Zhou D. McKelvey K.D. Apewokin S. Stephens O. Enderlin C.A. Vangsted A.J. Reed P.J. Anaissie E.J. GWAS of 972 autologous stem cell recipients with multiple myeloma identifies 11 genetic variants associated with chemotherapy-induced oral mucositis Support. Care Cancer 23 2015 841 849 10.1007/s00520-014-2406-x 25218607 PMC4371856

289 Kristinsson S.Y. Thrombosis in multiple myeloma Hematology. Am. Soc. Hematol. Educ. Program. 2010 437 444 10.1182/asheducation-2010.1.437 2010 21239832

290 Lee S. Lee C.H. Seo M.S. Yoo J.Il Integrative analyses of genes about venous thromboembolism: an umbrella review of systematic reviews and meta-analyses Medicine (Baltimore) 101 2022 E31162 10.1097/MD.0000000000031162 PMC9622592 36316870

291 Simone B. De Stefano V. Leoncini E. Zacho J. Martinelli I. Emmerich J. Rossi E. Folsom A.R. Almawi W.Y. Scarabin P.Y. Den Heijer M. Cushman M. Penco S. Vaya A. Angchaisuksiri P. Okumus G. Gemmati D. Cima S. Akar N. Oguzulgen K.I. Ducros V. Lichy C. Fernandez-Miranda C. Szczeklik A. Nieto J.A. Torres J.D. Le Cam-Duchez V. Ivanov P. Cantu-Brito C. Shmeleva V.M. Stegnar M. Ogunyemi D. Eid S.S. Nicolotti N. De Feo E. Ricciardi W. Boccia S. Risk of venous thromboembolism associated with single and combined effects of Factor V Leiden, prothrombin 20210A and methylenetethraydrofolate reductase C677T: a meta-analysis involving over 11,000 cases and 21,000 controls Eur. J. Epidemiol. 28 2013 621 647 10.1007/s10654-013-9825-8 23900608 PMC3935237

292 Manier S. Salem K.Z. Park J. Landau D.A. Getz G. Ghobrial I.M. Genomic complexity of multiple myeloma and its clinical implications Nature Reviews Clinical Oncology 14 2 2016 100 113 10.1038/nrclinonc.2016.122 201614 27531699

Funding

This work was supported by the Instituto de Salud Carlos III (Madrid, Spain; DTS23/00030 , PI17/02256 and PI20/01845 ), and from the Consejería de Salud y Familia de la Junta de Andalucía ( PY20/01282 ).

Abbreviated title: Genetic Architecture and Susceptibility in Multiple Myeloma