Introduction

With the advent of new therapeutics and the increasing utilization of high-dose melphalan and autologous stem cell transplantation (ASCT) over the last 20 years, 5- and 10-year overall survival (OS) have improved across all age, race, and ethnic groups in multiple myeloma (MM) [1]. These benefits are more tempered in those with high-risk disease with revised international staging system (R-ISS) stage III patients achieving only a 24% 5-year progression-free survival (PFS) and 40% 5-year OS [2]. It is critical to identify high-risk patients at diagnosis in order to move away from treatment adapted to patient's physiological/chronological age and comorbidities and rather toward the establishment of risk-adapted treatment approaches.

A uniform approach to risk in NDMM remains elusive despite several validated risk-stratification systems in routine clinical use. This is a direct consequence of our rapidly expanding ability to evaluate genomic level data as well as an ever-expanding amount of patient-level clinical data. The accurate assessment of risk at diagnosis is important for many reasons including but not limited to:

  1. 1.

    The longest remission period is achieved by initial therapy and thus the duration of the first remission is one of the most important factors impacting patient prognosis

  2. 2.

    Accurate definition of risk for clinical trial enrollment

  3. 3.

    Establishing which clinical data should be obtained routinely in practice to define risk.

There is significant heterogeneity in the various risk-stratification systems currently utilized as outlined in Table 1. While we attempt to capture risk at diagnosis, the reality is that many important prognostic characteristics remain ill-defined as some patients relapse early who were defined as low risk based on their genomic profile at diagnosis. The goal of this review paper is to summarize and compare the various established risk-stratification systems and go beyond the R-ISS and international myeloma working group (IMWG) risk stratifications to evaluate specific molecular and cytogenetic abnormalities and how they impact prognosis independently. We explore the wealth of new genomic information from recent whole-genome/-exome sequencing as well as gene-expression profile data and review known clinical factors impacting outcome such as disease burden and early relapse as well as patient-related factors. Finally, we provide an outlook on developing a new high-risk model system and how we might make sense of co-occurrences, oncogenic dependencies, and mutually exclusive mutations.

Table 1 Different methodologies of risk stratification.

General risk-stratification systems

The international staging system (ISS) is one of the earliest validated risk stratification for NDMM patients [3]. The ISS is a biological staging system predicting risk based on rising serum β2-microglobulin (β2M) and falling serum albumin. Subsequent to the ISS development, chromosomal abnormalities (CA) detected by interphase fluorescent in situ hybridization (iFISH) have become a standard of care in risk stratifying MM patients. Certain high-risk changes including del(17p), translocation t(4;14), and translocation t(14;16) have been established [4]. In 2014, the IMWG published an updated risk stratification focusing on differentiating high from standard-risk patients combining the ISS with certain high-risk iFISH changes including t(4;14), del17p13, and +1q21 [5].

The R-ISS combines iFISH changes, serum lactate dehydrogenase (LDH), and ISS features and is the most widely recognized risk-stratification tool for NDMM patients [2]. The R-ISS is a simple but clinically useful system predictive of both OS and PFS in NDMM. Although it incorporates important genomic markers including t(4;14), t(14;16), and del17p, it does not include 1q gain/amplification, an increasingly important prognostic marker [6], or mutational data from TP53. Importantly, in order to be R-ISS stage III, patients must also be ISS III with the biological marker β2M elevated to ≥ 5.5 mg/L. A significant portion of patients will be R-ISS stage I or II despite having high-risk iFISH changes. In a recent report by Corre et al. [7] evaluating del(17p) and TP53 mutations in NDMM patients, 73% of the patients with del(17p) alone and 52% of those with TP53 biallelic inactivation were not International Staging System (ISS)−3 and thus not classified in the R-ISS 3 subgroup. Further, β2M may indeed be a biological maker of high-risk disease but likely the inherent high-risk genomic features drive this as Bolli et al. found that 1q amplification correlated with higher β2M [8]. Finally, neither the R-ISS nor the IMWG weight cytogenetic findings. A recent report by the Intergrouped francophone Du Myelome (IFM) has shown that a weighted cytogenetic risk stratification based on certain high-risk lesions such as a del(17p), del(1p32), gain 1q, t(4;14), and trisomy 21 may have the ability to more accurately risk-stratify patients [9]. Unfortunately, the vast majority of patients included in this study were not treated with modern induction regimens. This brings up a frequent challenge when evaluating prognostic scores in NDMM given the quickly evolving treatment landscape and lack of treatment adjustment into currently utilized risk-stratification systems.

Beyond the R-ISS: molecular subgroups and cytogenetic abnormalities

In addition to traditional staging systems, there are well-established high-risk features in MM that portend to poor outcomes. These features include other molecular subgroups (primarily translocations into the immunoglobulin heavy-chain locus and copy number abnormalities (CNAs)) as well as new and emerging structural, mutational, and copy number drivers based on next generational sequencing. MM may have chromosomal aberrations carried by only a subset of tumor cells, and the cytogenetic heterogeneity of individual cases reflects the coexistence of cytogenetically defined aberrant plasma cell clones. A surrogate marker of clone size may include the percentage of cells harboring specific cytogenetic abnormalities detected by FISH. Although the European Myeloma Network (EMN) has recommended relatively conservative cutoff values of 10% for fusion or break apart probes and 20% for numerical abnormalities (similar cutoffs were utilized for the R-ISS staging system), so far no uniform cutoffs have been applied, and the cutoffs used in different centers are inconsistent.

Well-established molecular subgroups: translocations into the immunoglobulin heavy-chain locus

Most translocations into the immunoglobulin heavy-chain locus located at 14q32 are seen in greater than 40% of NDMM patients [4, 6]. The IgH locus at 14q32 is transcriptionally active in B cells, and the translocation of putative oncogenes to this region and their subsequent dysregulated expression is considered a seminal event in the pathogenesis of most B-cell malignancies, including MM [10]. There are several known translocations of 14q32 with nonrandom partners, including the more commonly observed t(4;14) and t(11;14) translocations (30% of patients with MM) and the less common (5% of patients) t(14;16), t(6;14), t(8;14), and t(14;20) translocations [10]. Each translocation subgroup is associated with deregulation of a D group cyclin either directly, such as occurs with the t(11;14) (cyclin D1) and t(6;14) (cyclin D3), or indirectly such as occurs with the t(4;14) or in the MAF translocation group which includes t(14;20) and t(14;16) [11]. These translocations ultimately lead to upregulation of oncogenes—including D-type cyclins (cyclin D1, D2 and D3), MAF family members (MafA, MafB, and c-Maf), c-MYC, the myeloma SET domain protein (MMSET), and the fibroblast growth factor receptor 3 (FGFR3)—and have been shown to influence patient prognosis.

Adverse

The MAF translocation group includes the t(14;16) and t(14;20), both of which are rare in MM, but are thought to be associated with poor prognosis. The mechanism of this poor outcome is thought to involve the consequences of MAF upregulation, which include upregulation of cyclin D2, and its effects on cell interaction and upregulation of apoptosis resistance [11]. t(4;14) translocation leads to mutation of the MMSET gene that is known to have histone methyltransferase activity and is deregulated early on in the genesis of developing MM [12]. t(8;14) and MYC aberrations/translocations lead to upregulation of the MYC oncogene. The prevalence, pathogenesis, and supporting literature for both 14q32 translocations and CNAs dictating risk varies and is outlined in Table 2.

Table 2 High-risk molecular and cytogenetic subgroups; balanced translocations into the immunoglobulin heavy-chain locus and copy number abnormalities.

Well-established molecular subgroups: copy number abnormalities

Additional copy number gains and losses occur frequently with the most frequent being del 13q (59%), +1q (40%), del14q (39%), del6q (33%), del1p (30%), and del17p (8%). Table 2 outlines key features of CNAs with special attention below to 1q gain/amplification and del(17p) as these likely represent the most deleterious genomic changes in NDMM.

1q amplification (v gain)

The gain/amplification of CKS1B gene at chromosome region 1q21 (1q+) is one of the most common secondary genetic abnormalities in MM and is seen in about one-third of NDMM patients [7]. CKS1B is an essential protein for cell growth and division and is a member of the cyclin kinase subunit 1 protein family. It is expressed universally in the bone marrow and associates with p27kip1-Cdk/cyclin complex and acts as a cofactor for Skp2-dependent ubiquitination of p27 [13]. An amplified CKS1B results in greater degradation of p27, activation of the Cdk/cyclin complex, and cell cycle upregulation by promoting the G1/S transition and plays a critical role in cell cycle progression and MM cell survival.

Various 1q states are seen in NDMM patients including diploid, gain of 1q (three copies of 1q), and amplification of 1q (≥4 copies of 1q). The differential impact on prognosis between gain and amplification remains to be completely elucidated but any additional copies of 1q has been shown to lead to inferior outcomes. The impact of copy number on long-term outcomes is variable but ≥4 copies or amplification typically drives the most dismal PFS and OS [6]. While many postulate that del(17p)/TP53 mutation is the most impactful driver of prognosis, the recently updated data on 1q amplification from the FORTE trial calls this into question where an intensified treatment approach improved outcomes in all groups save those with 1q amplification [14].

del(17p)

Cytogenetic analysis of chromosome 17p deletions which spans the TP53 gene is typically performed by iFISH probes against 17p and does not probe TP53 in isolation. Although the clinical relevance of del17p is well established in MM, the exact mechanism by which del17p promotes aggressive disease biology remains unclear. As in other tumor types, TP53 mutations in MM are spread across the entire gene, with many mutations occurring within the DNA-binding domain [15]. The length of the deleted region can vary from a few megabases (MBs) to deletion of the entire short arm of chromosome 17. The TP53 gene is located in the minimally deleted region (0.25 MB) suggesting that it is a critical gene in the 17p13 region. However, a deletion event usually involves several genes and co-deletion of TP53 along with Eif5a and Alox15b has resulted in more aggressive disease [15]. It remains unclear how genes other than TP53 contribute to tumorigenesis. Missense mutations of TP53 might associate with even worse outcome in some cases as they produce mutant TP53 proteins that not only result in loss of normal TP53 function but also gain of oncogenic functions [16]. From the myeloma genome project (MGP), Walker et al. demonstrated that TP53 deletion is the most common abnormality at 8%, followed by mutation (~6%) and biallelic inactivation (~4%). Of note, TP53 mutation has been identified as a driver mutation in MM and is one of the few driver mutations with prognostic power [17].

Early studies suggested an association between deletion on one allele and mutation on the second allele putatively resulting in complete inactivation of P53 function [18]. The relationship between mono and biallelic del(17p) and TP53 mutational status remains to be clarified and Table 3 summarizes the known prognosis of biallelic vs. haploinsufficiency. Further, what defines a positive test for del(17p) remains controversial with cancer clone fraction (CCF) positivity rates vary based on cutoffs. The known impact of CCF is also summarized in Table 3.

Table 3 del(17p) biallelic vs. haploinsufficiency and cancer clone fraction (CCF).

The use of different thresholds/CCFs, different size datasets, as well as different treatment regimens have resulted in discordance in the reported prognosis of del17p. Regardless, when detected del(17p) is ubiquitously adverse. The R-ISS, IMWG, and mSMART staging systems as well as whole-genome/-exome sequencing data from both the myeloma genome project [6] as well as the IMWG CoMMpass study [19] have all clearly shown dismal outcomes in del(17p) patients. When incorporating RNA alterations and gene-expression profiling it remains predictive of both PFS and OS as well.

Hyperdiploid, tetraploid, and trisomies

Hypodiploid karyotypes or hyperhaploid karyotypes are associated with an adverse prognosis in NDMM. Tetraploidy is an independent marker associated with significantly shorter OS [20]. It is well described that several high-risk lesions frequently co-occur with standard-risk patients and that hyperdiploid myeloma (HD-MM), although generally agreed upon to be protective [21], is biological heterogeneous as exemplified by the fact that 78% of IgL-MYC translocations co-occur with HD-MM [22]. Further, among HD-MM, patients with trisomy 21 have poor outcomes [23] although this is controversial and being increasingly challenged.

The challenge and applicability of traditional iFISH risk stratification

The IMWG consensus statement describes clinical iFISH as the standard approach for detecting CAs and the R-ISS staging system followed the same methodology. However, within the R-ISS inconsistencies existed in defining positive cytogenetic abnormalities and the cutoff levels were not identical ranging from 8 to 20% for numerical aberrations and from 10 to 15% for immunoglobulin heavy-chain translocations. Further, in routine clinical practice, more heterogeneity exists with some labs not performing the required purification or dual staining and as with the R-ISS data the detection limits and positivity thresholds vary between institutions. This heterogeneity may limit the utility of the R-ISS and IMWG staging systems particularly when applied after collaborating data from multiple institutions. More recently, extensive collections of MM genomic data are being utilized to further elucidate risk in NDMM patients but they too have not escaped this challenge. For example, the CoMMpass study (NCT01454297) has provided an unprecedented platform for genomics and outcomes research in MM but one of the few critiques stems from the heterogeneity in cytogenetic analysis. In an audit of the top ten recruiting sites, significant discordance was found between the local data extraction and their central audit with variability in the FISH probes utilized, number of cells counted, and sorting techniques [24]. Of note, traditional FISH studies are quite expensive further motivating the field to move beyond traditional FISH studies toward next-generation sequencing tools.

Seq-FISH with next-generation sequencing tests can be designed to simultaneously detect the copy number abnormalities and translocations detected by clinical FISH along with gene mutations that cannot. From the CoMMpass study, Goldsmith et al identified 672 patients with sufficient data to calculate R-ISS via Seq-FISH technique using calls on whole-genome sequencing (WGS) long-insert data with the threshold for a positive detection of a CNA by Seq-FISH being 20%. The R-ISS-NGS resulted in significant redistribution of patients from stage I into stage II. R-ISS-NGS stages II and III were associated with worse PFS and OS more so than the staging schema of the R-ISS [24]. Further, Miller et al. evaluated 339 patients also from the CoMMpass study and found Seq-FISH identified nearly all translocations as well as 30 translocations missed by clinical FISH [25]. Thus Seq-FISH has validated the prognostic power of the R-ISS and increased the sensitivity and reproducibility of identifying CAs. However, like gene-expression profiling described in detail below, the clinical application remains challenging given the laboratory experience and capabilities required as well as turnaround time in routine clinical practice.

Making sense of co-occurrences, oncogenic dependencies, and mutually exclusive mutations

As more samples are sequenced in MM, co-occurrences or oncogenic dependencies between genomic markers are being increasingly described [6, 26]. This makes an exact assessment of the impact of specific cytogenetic abnormalities difficult especially when these abnormalities are considered in isolation and or when they are rare events such as t(8;14) or t(14;16). Prior to our ability to readily perform whole-genome sequencing, the number of known oncogenic dependencies were limited. However, large datasets such as the Myeloma Genome Project and the CoMMpass project have increased our awareness of co-occurring events. The co-segregation of these adverse prognostic factors emphasizes the need to adjust for potential confounding and should lead to improved risk stratification in NDMM patients. Further, understanding the biology of the tumors and how particular co-dependencies function and their potential reliance on similar pathways may lead to identifying new therapeutic targets.

Whole-genome/-exome sequencing

Next-generation sequencing (NGS) technologies have allowed the identification of RNA transcript expression, genomic structural variants (translocations, deletions, insertions, inversions), single nucleotide variants, loss of heterozygosity, and copy number abnormalities affecting whole chromosomes, segments of chromosomes, and individual genes. Dozens of myeloma driver genes have been identified with the most common occurring in the RAS and NF-kB families [27]. Chromothripsis, a genomc event that leads to massive, clustered genomic rearrangements, is an emerging high-risk signature that is just recently being described. With newer technologies making whole-exome and whole-genome sequencing more readily available and less expensive, the ability to complete more comprehensive genomic profiling of MM patients is increasingly becoming a reality. This has renewed the importance of identifying and prognosticating driver mutations and additional genetic variants that might lead to improved patient expectations and ultimately therapeutic advancements.

The MGP, CoMMpass study, as well as work done by a collaboration of US and European centers published by Bolli et al. [8] has expanded our knowledge of the genomic environment in which MM develops and importantly identified novel risk factors leading to poor outcomes. Several conclusions can be safely made after reviewing this data including:

  • del(17p)/TP53 mutations a well as +1q amplification are powerful drivers of poor prognosis

  • Many novel driver and oncogenic genes remain to be explored

  • Loss of heterozygosity [6, 17] (LOH) and an APOEBEC [6, 17] signature impact prognosis

  • Burden of driver gene and overall somatic missense mutation drive poor outcomes

  • Genomic clusters exist and dictate prognosis

  • Certain genomic pairings leading to "double hit" genotypes dictate dismal outcomes.

Table 4 summarizes key findings in the most recently reported large patient datasets with whole-genome/-exome sequencing available.

Table 4 Whole-genome/-exome sequencing.

RNA and gene-expression profiling

Given DNA-based assays such as whole genomic sequencing are able to identify individual lesions and markers of global genomic instability and ultimately prognosis, it is not surprising that the development and now validation of several GEP scoring systems have shown strong prognostic value. Most studies have identified GEP signatures as an independent prognostic factor although overlap with clinical and iFISH/cytogenetic risk factors do exist [28,29,30]. The HOVON-65/GMMG-HD4 clinical trial researchers and University of Arkansas for Medical Sciences (UAMS) researchers have reported a 92 [30] and 70-gene signature [28], respectively, able to identify poor outcome in independent cohorts. Although a variety of other GEP have been developed [31], only two have matured into validated clinical tests: MMprofiler (EMC92/SYK92) and MyPRS (UAMS GEP70).

EMC92/SKY92/MMprofiler

This GEP was originally developed from newly diagnosed MM patients included in the HOVON-65/GMMG-HD4 trial (n = 290) [30]. A prognostic signature of 92 genes (EMC92-gene signature) was generated with high-risk defined as OS of less than 2 years (63 out of 290 patients—21.7%) generating a two-tier system of high and standard-risk populations. The EMC92 was then validated in several up-front MM patient cohorts including total therapy (TT)2 (19.4% at high risk), TT3 (16.2% at high risk) and MRC-IX (20.2% at high risk). Multivariate analysis was performed in the training set and in the MRC-IX validation sets which showed that in addition to the EMC92 signature, del(17p) and β2M were also independent predictors in HOVON-65/GMMG-HD4. The SYK92 MMprofiler would go on to be validated in other in NDMM settings including patients receiving up-front KRD induction with and without ASCT consolidation [32]; 329 patients from the NRCI Myeloma XI trial [29]; and specifically in elderly non-transplant eligible patients [33].

The UAMS GEP70 or MyPRS

In one of the earliest GEP studies, Shaughnessy et al reported on a 70-gene scoring system in 532 NDMM patients [28]. Both the training and validation groups were treated on National Institutes of Health (NIH)–sponsored clinical trials UARK 98–026 and UARK 03–033, respectively. Both protocols used chemotherapy-based induction regimens followed by melphalan-based tandem autotransplantation, consolidation chemotherapy, and maintenance treatment. They identified a high-risk group that comprised 13.4% of patients and exhibited significantly inferior event-free survival (EFS)(P = 0.001; HR of 4.51) and OS (P = 0.001; HR of 5.16). On multivariable analyses for OS and EFS controlling for ISS risk and high-risk translocations, the high-risk UAMS GEP70 score retained its significance (HR = 4.1; P = 0.001). As with the SYK92, this has now been validated in several cohorts including the same 329 NDMM patients treated on the NCRI Myeloma XI trial as well as 456 patients treated on the GMMG-MM5 trial [34].

Is GEP ready for prime time?

Despite growing evidence of its prognostic value, the application to routine clinical care remains challenging. There is no consensus on a universal adaptation and none are validated by the FDA. Chng et al. attempted to evaluate the optimal GEP for MM by examining patients from three publically available GEP datasets [35]. They evaluated nine GEP profiles looking at all non-redundant combinations and constructed all possible combinations of multiple signatures up to nine full signatures and performed survival analysis for each combination. They demonstrated reproducibility across the nine systems, thus GEP can capture core biology that is not a result of random methodological artifact. They showed that the EMC92+HZDCD combination provides highly improved performance compared with other signatures or combinations. Others have shown that the SYK92 [36] or a combination of the EMC92 and the ISS (referred to as the EMC92-ISS) may be the optimal system [37]. With a rapidly changing therapeutic landscape, re-validation will be necessary. Capturing clonal content and evolution remains a challenge and newer high-throughput technologies are needed along with newer bioinformatics methodologies to identify meaning from the large amount of data being generated. Many unanswered questions still exist such as different GEP mutual relationships, the utilization of multiple systems, and the possibility of outperforming combinations. Nevertheless, targeted NGS approaches allow the assessment of all copy number variations, IGH translocations, and recurrent mutations in one technique. Thus, likely this technology has significant advantages in the long term [35,36,37].

Beyond the R-ISS: high-risk clinical features

Clinical and biological features have prognostic value beyond genomics in NDMM patients. Tumor burden dictates risk and was included in the original ISS staging system [3]. Subsequently, malignant plasma cells in the bone marrow and peripheral blood have also been shown to be prognostic. The plasma cell proliferation index (PCPI), a measure of plasma cell proliferative activity, has shown an association between metaphase cytogenetic abnormalities and rapid myeloma cell proliferation and ultimately clinical outcomes [38]. Focal myeloma lesions and extramedullary disease have also been shown to predict clinical outcomes. However, questions remain regarding the potential confounding of genomics on these high-risk biological and disease burden-related risk factors. Disease burden and patient-related factors depicting risk are outlined in Fig. 1.

Fig. 1: High-risk clinical features.
figure 1

*Large FLs (diameter >2.5 cm) associated with site-specific enrichment of HiR driver mutations consistent with them being key mediators of drug resistance and treatment failure [86,87,88,89,90,91,92,93,94,95,96,97,98,99,100]. **Certain EME sites seemed to carry worse prognosis with 3-year PFS differing according to involved organs: kidney (59.5%), skin (20.1%), lymph nodes (37.6%), CNS (47.9%), lung/respiratory tract (44.4%), GI/liver (22.5%), and spleen, ovaries, and testes (60.0%). BM bone marrow, CA cytogenetic abnormalities, CPCs circulating plasma cells, EBMT European Society for Blood and Marrow Transplantation, EME extramedullary myeloma that is extra-osseous (results from hematogenous spread and involving only soft tissues, the incidence in NDMM 1.7–3.5%90), EMB extamedullary myeloma that is paraskeletal or paraosseous plasmacytomas (consists of tumor masses adjacent to bones and arising from focal skeletal lesions, incidence in NDMM 6–34.4%90), EMM extramedullary myeloma, FL focal lesion, HR hazard ratio, ISS international staging system, MRI magnetic resonance imaging, MV multivariate, NDMM newly diagnoses multiple myeloma, NR not reached, OS overall survival, PC plasma cells, PCPI plasma cell proliferation index, PET-CT 18-fluoro-deoxyglucose emission tomography, PFS progression-free survival, R-ISS revised international staging system, TT total therapy.

Patient-related factors

In addition to risk-stratification systems, genomic features, and disease burden, additional non-modifiable patient-related factors affect outcomes in MM. Clinical frailty and geriatric assessments have been shown to impact outcomes in MM but their routine use has been largely limited due to clinical time restraints. In a recent systemic review and meta-analysis, a significantly increased HR for death was shown for patients with activity of daily living score ≤4 (pooled HR = 1.576; 95% CI, 1.051–2.102) [39]. Further, patients classified as frail showed higher risk of death than fit patients did (pooled HR = 2.169; 95% CI, 1.002–2.336). It is of note though that genomic risk may be intimately related with patient-related factors. In 1777 NDMM patients treated on the Myeloma XI trial, patients with TP53 deletion showed features of advanced disease and associated morbidity, specifically poorer performance status (World Health Organization [WHO] performance status ≥2; P = 0.0012). Although WHO performance status was independently associated with shorter survival, the association with TP53 deletion suggests an interrelationship with genetic and clinical features [40].

There is increasing evidence that socioeconomics and access to care directly impact patient outcomes. Several studies have demonstrated that patients of minority ethnic or racial background are less likely than non-Hispanic Whites (nHws) to receive ASCT as treatment for MM and that referral for transplantation may be delayed. However, similar outcomes for minorities compared with nHws undergoing ASCT has been shown when access is equal [41]. MM patients of racial and ethnic minority are frequently underrepresented in clinical trials. Pulte et al. performed a meta-analysis evaluating patients on five recent clinical trials that utilized novel agents and did not find a difference in outcome based on race. Because Hispanic and African American patients have the least apparent benefit from newer agents at the population level. These results suggest that minority patients are less likely to be appropriately treated [42]. To further validate this point, a recent VA experience showed that with equal access, AA patients may have superior outcomes with median OS of AA patients 5.07 years (95% CI, 4.70–5.44 years) as opposed to 4.52 years (95% CI, 4.38–4.65 years) for white veterans (log-rank P < 0.001) [43].

Biology of disease trumps everything

Response to initial therapy and achieving a prolonged initial remission duration may ultimately be the most important prognostic factor in NDMM patients. There is clear data that shows achieving deep remissions that are minimal residual disease (MRD) negative can trump high-risk biological features and that standard-risk patients who fail to achieve deep remissions fair worse and may indeed be high risk after all [44]. Below, we will briefly review the data on primary refractory and early relapsing myeloma but will forgo an in-depth review of MRD and its impact on outcomes as this topic has been covered extensively in several recent reviews and meta-analyses.

Response rates to standard triplet induction therapy for both transplant eligible and ineligible patients are in the 85–90% range [45] thus primary refractory myeloma is uncommon. Unfortunately, despite improved 2nd line therapy, outcomes for these patients remain poor even if treated with novel induction. For patients undergoing up-front ASCT after induction failure, as far back as 2010 Gertz et al. showed that failure to achieve at least a partial response (PR) to IMID based induction prior to ASCT leads to shorter OS (73.5 vs. 30.4 months) and PFS (22.1 vs. 13.1 months; P < 0.001) from time of transplant [46]. Lee et al. demonstrated even worse outcomes in patients refractory to novel based regimens (majority were bortezomib based) showing a median PFS of 4.7 months and median OS of 11.6 months following ASCT [47]. Although there is limited data in transplant ineligible or deferred patients, the same pattern holds. For example, in an updated analysis from the mayo clinic amongst patients treated with novel induction regimens, primary refractory patients had a far inferior median OS of just 3.6 vs. 7.9 years (P < 0.001) [48].

Early relapse is likely a reflection of the underlying high-risk disease biology that was not captured in the initial risk assessment and leads to inferior outcomes regardless of cytogenetic risk. Durie et al. were the first to show that the underlying dominant predictor for survival is time to progression [49] and the Mayo Clinic was the first to describe the adverse prognostic impact of an early relapse after intensive strategy [50]. In a Center for International Blood and Marrow Transplant Research (CIBMTR) analyses of 3256 NDMM patients from 2001 to 2013 who received up-front ASCT, the proportion of patients relapsing within 24 months following ASCT was stable over time at 35–38%. The OS from the time of relapse was significantly inferior for the early relapse group with a 4-year OS of 30% vs. 41% (P<0.001) [51]. Relapse within 1 year of ASCT leads to even worse outcome with Kastritis et al. showing that among 297 consecutive NDMM patients receiving first-line ASCT, 43(14.5%) relapsed within 12 months and had dismal outcomes with median post-ASCT survival of 18 months vs. >6 years (P<0.001) in late relapsing patients [51]. These outcomes unfortunately have not improved much with an older cohort from the Mayo clinic showing just a 23.9-month median OS [52].

Patients not eligible for up-front ASCT who relapse early also do poorly. In a cohort of 511 NDMM patients, Majithia et al. showed that in 82 patients (16%) who relapsed within one year of therapy, the median OS was 21.0 months vs. NR (P<0.001). The survival disadvantage persisted even when considering only patients who received subsequent therapies with a median OS of 26.7 months vs. NR (P<0.001) [53]. Finally, a recent IFM report showed that early relapse after first-line therapy still negatively impacts survival even when controlled for genomic factors [7]. Interestingly, approximately two-thirds of early relapsing patients in this IFM cohort were not initially considered high risk and thus early relapse trumps genomic risk.

Conclusion: developing a new high-risk model and future directions

The myeloma research community has amassed a vast expanse of genomic data from NDMM patients over the last decade. This has led to significant advances in our understanding of the genomic changes that portend to poor outcomes in NDMM patients. Unfortunately, our success in elucidating high-risk genomic features in NDMM patients has not translated into tailored therapeutics and improved outcomes in these patients. An up-to-date uniform consensus on high-risk features is overdue and expected soon from the IMWG. Table 5 outlines our current stance on high-risk features in NDMM patients. Certain features, such as GEP, whole-genome sequencing, and PCLI may not be applicable in routine clinical practice but nonetheless have been consistently shown to drive poor outcomes. More comprehensive and routinely obtained genomic profiling beyond traditional FISH is needed to advance risk stratification in NDMM. We would consider any NDMM patient that meets any of the criteria listed in the high-risk column as being a high-risk patient and strongly encourage enrollment onto clinical trials for these patients.

Table 5 High-risk features for newly diagnosed multiple myeloma.

In order to properly risk-stratify patients in routine clinical care, we recommend obtaining the following at diagnosis prior to initiating therapy:

  • Serum studies: LDH, β2-microglobulin, albumin

  • Imaging: skeletal survey, advanced bone imaging ideally PET-CT (alternatively whole-body CT, MRI spine and pelvis)

  • Bone marrow biopsy: standard cytogenetics, iFISH myeloma panel, clonoseq MRD ID specimen, GEP, and PCPI as able

  • Frailty/performance status and socioeconomic barriers to care.

MM is a genomically complex disease with diverse clinical outcomes based on the genomic footprint of each individual patient. The international collaboration of MM practitioners has advanced both our biological understanding of risk in myeloma and has led to improved treatment outcomes overall. Moving forward, several challenges remain and ongoing large-scale collaboration will be needed to overcome them. We must begin a more concerted effort to translate our knowledge of high-risk genomic features into improved clinical outcomes by tailoring therapeutics to risk. The standardization of iFISH methodology and importantly the definition of positive results is needed. We must move to incorporate GEP and possibly PCLI into routine clinical care not just at large academic centers and as part of clinical trials. We must better incorporate objective measurements of patient-related factors into our risk assessment and treatment approach. Finally, we must address access to myeloma care to overcome socioeconomic barriers to care that have led to inferior outcomes in ethnic minorities diagnosed with MM. These challenges are immense but with ongoing collaboration, they can be achieved in time.