Next-generation sequencing-based high-risk genomic stratification in multiple myeloma

Multiple myeloma (MM) is the second most common lymphoid malignancy, with an estimated 176,000 new cases diagnosed worldwide each year (1). In the United States, approximately 36,000 new cases are diagnosed annually (2). Although therapeutic advances over the past several decades have substantially improved patient outcomes, a major challenge remains regarding how to address the heterogeneity in terms of clinical outcomes. For instance, some transplant-eligible patients may achieve progression-free survival (PFS) greater than 10 years, while other patients may experience rapid progression and die within only a few years after diagnosis (3-5). This dramatic heterogeneity is driven, in large part, by the underlying cytogenetic abnormalities of malignant plasma cells (3-6). The 2015 International Myeloma Working Group (IMWG) criteria defined high-risk disease by the presence of del(17p), t(4;14), or t(14;16) (6). However, after almost a decade of clinical applications based on the 2015 version of risk stratification, there have been some controversies regarding the genomic features selected to define high risk. Of note, while del(17p) has remained a strong adverse prognostic factor, the prognostic impact of translocations—t(4;14) and t(14;16) appears to be more context-dependent than originally appreciated (7-9). These concerns, together with growing recognition that additional lesions such as 1q gain and TP53 abnormalities carry important prognostic value, have contributed to the refinement of risk stratification frameworks in MM, including ISS (International Staging System), R-ISS (Revision of ISS), and R2-ISS (Second Revision of ISS) (7-9).

Therefore, after almost a decade of worldwide practice of the 2015 high-risk criteria, the International Myeloma Society (IMS) and the IMWG recently proposed a revised genomic model for high-risk MM, i.e., the Consensus Genomic Staging (CGS) (9). Under this updated framework, patients are classified as high risk if they present del(17p), a TP53 mutation, biallelic del(1p32), or a combination of t(4;14), t(14;16), or t(14;20) with either 1q gain or a monoallelic del(1p32), as well as the combination of 1q gain and monoallelic del(1p32) (9). In addition, under the new criteria, patients without these genomic abnormalities may still be classified as high risk if they have elevated β₂-microglobulin in the context of normal renal function (9).

The primary goal of the Blood study by Schavgoulidze et al. was to validate the CGS. Specifically, they sequenced 6,528 patients with newly diagnosed MM and 1,583 patients at first relapse, all recruited between 2019 and 2024 in France (10). In terms of techniques, because some of the genomic features included in the CGS, such as TP53 mutations and biallelic del(1p32), cannot be adequately assessed by conventional fluorescence in situ hybridization (FISH) alone, they implemented a targeted next-generation sequencing (NGS) panel (10). Briefly, this panel was designed to cover all newly defined high-risk genomic markers proposed by the CGS, including translocations, mutations, and genetic variants. In routine clinical practice, they applied this panel to define the high-risk genomic landscape in both newly diagnosed patients and patients at first relapse (11).

Overall, for the analyzed 6,528 patients at diagnosis and 1,583 patients at first relapse, the median follow-up was 35 months. Among the 6,528 patients with newly diagnosed MM, 1,463 (22.4%) were classified as high risk according to the genomic features proposed by the CGS. They further characterized the distribution of genomic factors within high-risk patients, showing that del(17p) was the most common lesion, whereas isolated TP53 mutation represented the second most frequent defining marker. Because the CGS also incorporates elevated β₂-microglobulin in the context of normal renal function as a high-risk factor, the inclusion of this non-genetic factor identified an extra 7.3% of patients beyond those classified as high risk by genomic factors alone. As expected, the proportion of high-risk genomic profiles was even higher in patients at first relapse, reaching 36.7%. A broadly similar distribution of high-risk genomic factors was observed in the relapse setting, with del(17p) remaining the most frequent abnormality, and TP53 mutations also commonly detected.

They then analyzed the PFS among patients with newly diagnosed MM who had at least 18 months of follow-up or died/progressed within 18 months. Limited by the follow-up period since the beginning of their NGS analysis, overall survival (OS) was not analyzed as an endpoint in the current study. In the subset of patients with available PFS data, after a median follow-up of 35 months, they confirmed that the median PFS was 51 months in patients classified as standard risk, compared with 30 months in those classified as high risk by the CGS. Interestingly, the CGS criteria detected the smallest proportion of patients classified as not high-risk who nevertheless experienced first relapse (13.5%), compared with the ISS, R-ISS, and R2-ISS. Although genomic risk factors alone apparently did not capture all high-risk patients, their findings suggested that the CGS criteria improved risk stratification relative to previous criteria. In addition, they found a small number of patients would be reclassified when the CGS was applied instead of the R-ISS, further supporting the clinical relevance of the revised framework of patient stratification.

They further investigated the prognostic impact of each individual high-risk feature that was included in the CGS using the standard-risk population as control. They found that the association of del(17p) with TP53 mutations, i.e., double hit dramatically worsened disease progression. They also confirmed the previously reported ultra-high-risk category—a biallelic del(1p32) to have an even lower PFS of only 15 months relative to the standard-risk controls (51 months). In addition, they also found that those patients with 1q gain together with either translocations—t(4;14) or t(14;16) showed significantly shorter PFS relative to standard-risk controls.

Therefore, through systematic analysis of a large MM cohort and by taking advantage of their uniquely rich MM resources, the authors were able to validate the recent IMS/IMWG genomic consensus definition of high-risk MM. The primary message from this study was that approximately 22% of patients with newly diagnosed MM were classified as high risk and the high-risk classification was significantly associated with lower PFS, including among patients with MM who received transplantation. For the myeloma research community, findings from the current study may have several important implications. Firstly, the authors demonstrated that targeted NGS can be incorporated into routine clinical practice for CGS-based risk assessment. At the same time, broader implementation should not be assumed to be straightforward, as real-world adoption will depend on cost, laboratory standardization, bioinformatic infrastructure, reporting consistency, turnaround time, and reimbursement (11-13). Secondly, the findings showed some improvement in terms of risk classification, i.e., fewer patients with first relapse were classified as not high-risk using the CGS compared to ISS, R-ISS, and R2-ISS criteria. Thirdly, the study suggested that elevated β₂-microglobulin in the context of normal renal function may add prognostic information beyond genomic lesions alone. At the same time, the inclusion of elevated β₂-microglobulin in a framework designated as genomic staging raises a conceptual question. Although clinically pragmatic, this choice blends biochemical and genomic information, and may partly overlap with the prognostic information already captured by ISS- or R-ISS-based models. Given the limited additional separation observed in this study, the precise role of this non-genomic component within CGS remains to be clarified. Nevertheless, this work provided a valuable foundation for future studies, e.g., to test the discriminatory ability of the CGS criteria in clinical trials for immunotherapies in first-line treatment.

More broadly, CGS is perhaps best viewed not as a complete replacement of prior staging systems such as the R-ISS or R2-ISS, but as a more genomically refined framework for identifying biologically high-risk disease. Its main contribution is that it moves beyond a limited set of conventional cytogenetic markers and captures additional lesions and lesion combinations that carry clear prognostic relevance. At the same time, genomic classification at diagnosis is unlikely to be sufficient on its own in contemporary myeloma care. Risk is increasingly shaped not only by baseline genomic features, but also by treatment response, measurable residual disease status, imaging findings, circulating blood-based biomarkers such as circulating tumor DNA and circulating plasma cells, immune microenvironment-related features, and clonal evolution over time (14-17). These complementary factors capture disease burden, treatment sensitivity, dissemination, and host-tumor interaction in ways that static baseline genomic classification cannot fully reflect. In this context, the value of redefining high-risk MM may lie less in assigning a fixed prognostic label and more in identifying patients who may require closer monitoring, risk-adapted treatment strategies, and prioritization for studies testing intensified or immunotherapy-based approaches.

There were, however, several limitations that should be addressed in future studies. First, it is well recognized that patients with myeloma show important racial and ethnic differences in incidence, biology, treatment access, and clinical outcomes (18-21). For example, in the United States, patients of African ancestry have a higher disease burden and may experience different clinical outcomes than patients of European ancestry, particularly when differences in access to therapy are taken into account (18-21). It is not clear how the CGS criteria performed in terms of predicting PFS or OS in different populations, e.g., African Americans versus European Americans. This question is especially relevant because emerging molecular studies have suggested population-specific disease biology in MM, including e.g., epigenetic differences detectable through circulating cell-free DNA (22,23). Second, because of the relatively recent implementation of NGS-based assessment and limited follow-up period, the authors were not able to determine whether the CGS risk classification could also improve prediction of all-cause OS. Future studies will be needed to address the relevance of the CGS criteria with OS, compared to previous standards. Third, although the study supports the clinical feasibility of targeted NGS for CGS assignment, implementation across different practice settings may remain uneven until issues related to standardization, accessibility, turnaround time, reporting, reimbursement, analytic infrastructure, and prospective validation are more fully addressed.

In summary, this large-scale retrospective clinical study provided important validation of the new CGS framework for risk classification for patients with MM by confirming its predictive value for PFS, and suggesting improved discrimination relative to previous standards. Although several questions remain unresolved, such as population specificity, predictive value for OS, the findings from the current study reassured the research community to start implementing the new criteria in the care of patients with MM.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Translational Cancer Research. The article has undergone external peer review.

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0976/prf

Funding: This work was partially supported by grants from the National Institutes of Health (Nos. R01CA280637, R01CA223662, and R21CA283588).

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0976/coif). W.Z. reports this work was supported, in part, by grants from the US National Institutes of Health (Nos. R01CA280637, R01CA223662, and R21CA283588). The other author has no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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