МСМ-ММРГ 2025 Консенсусная геномная установка предсказывает результаты с помощью четырехструнных режимов, основанных на дратумумабе, для NDMM.

Introduction

Prognostic risk scores for newly diagnosed multiple myeloma (NDMM) have undergone several iterations, with repeat validation required with the advent of more effective therapies. The Durie-Salmon criteria, developed in the era of conventional chemotherapy, included measures of tumor mass and associated organ damage (anemia, renal impairment, hypercalcemia, and bone disease). 1 This was succeeded in 2005 by the International Staging System (ISS), comprising only serum albumin and β-2-microglobulin (B2M). 2 The ISS is easily applicable across varied clinical settings and is routinely assessed in recent clinical trials. 3 , 4 , 5 , 6 , 7

A revised ISS (R-ISS) score in 2015 included 3 adverse genomic features; del(17p), t(4;14), and t(14;16), and has become commonly used in clinical trial stratification and data reporting. 3 , 4 , 8 , 9 Although both the ISS and R-ISS are accurate in defining a small high-risk (HR) patient cohort, most patients are assigned to low- and intermediate- risk groups. 2 , 8 In addition, it has been widely reported that to harboring >1 risk factor (RF) has either an additive or compounding effect on clinical outcomes. 10 , 11 , 12 This led to a second revision (R2-ISS) in 2022, which combines the risk for each of ISS, high lactate dehydrogenase, del(17p), gain1q, and t(4;14), providing a 4-tier risk score. 13 Although the R2-ISS has been validated with recent clinical trial data, 14 it has not been routinely adopted into clinical practice. 6 , 7

In 2023, the International Myeloma Society (IMS) and the International Myeloma Working Group (IMWG) convened an expert panel to consider all available data and devise a consensus definition for HR NDMM, which was recently published. 15 The consensus genomic staging (CGS) includes TP53 mutation status, representing the first inclusion of genomic sequencing data in prognostic scores intended for clinical use. The criteria are as follows: (1) del(17p), with a cutoff of >20% cancer clonal fraction (CCF), and/or TP53 mutation; (2) an immunoglobulin H (IgH) translocation including t(4;14), t(14;16), or t(14;20) along with gain1q (1q+) and/or del(1p32); (3) monoallelic del(1p32) along with 1q+ or biallelic del(1p32); and (4) B2M of ≥5.5 mg/L with normal creatinine (<1.2 mg/dL). 15

Because most centers currently assess NDMM using fluorescence in situ hybridization (FISH) rather than sequencing, TP53 mutation data have not historically been routinely available. Published series with the latest 4-drug induction regimens; daratumumab-lenalidomide-dexamethasone with bortezomib (DVRd) or carfilzomib (DKRd), collectively referred to as dara-quads, have limited genomic characterization available. 3 , 16 , 17 , 18 Although both the GRIFFIN 3 (DVRd) and MASTER 11 (DKRd) trials were considered when defining the CGS, TP53 mutation data were not included from these data sets. Here, we apply the IMS-IMWG CGS to a large series of patients treated with dara-quads at Memorial Sloan Kettering Cancer Center (MSKCC), to assess the prediction of response and survival outcomes in this clinical setting.

Methods

Patients

The MSKCC Myeloma Service database was interrogated for all patients with NDMM treated with dara-quads, specifically DVRd or DKRd, with a minimum of 6 months of clinical follow-up data available. Patients were excluded if they had >1 cycle of alternative therapy before receiving dara-quads, with localized radiation therapy being allowed.

Treatment

The initial 69 patients who received DKRd were enrolled on the MANHATTAN clinical trial (ClinicalTrials.gov identifier: NCT03290950 ), 18 which was conducted in accordance with the International Conference on Harmonization Good Clinical Practice guidelines, the principles originating from the Declaration of Helsinki, and MSKCC regulations. On trial, patients achieving minimal residual disease (MRD)-negativity after 8 cycles of induction were able to delay upfront autologous stem cell transplantation (ASCT).

The remainder of patients were treated per the institutional standard-of care (SOC) approach. The number of induction cycles varied at physician discretion, with a median of 4 cycles administered before restaging and consideration of ASCT. Most commonly, daratumumab was given as 1800 mg by subcutaneous injection, with dosing frequency per the prescribing information, bortezomib was given as 1.3 mg/m 2 subcutaneously on days 1, 8, and 15 of 28-day cycles, and carfilzomib was given as 56 mg/m 2 IV on days 1, 8, and 15. Overall, 19 patients (4%) switched between DVRd and DKRd after ≥2 cycles, without progression events, and were considered according to the initial therapy prescribed.

Maintenance was at treating physician’s discretion, and was continued in most until cancer relapse, progression, or intolerance per SOC. Some patients were enrolled on additional clinical trials during the follow-up period, including a quality-of-life study randomizing patients between daratumumab and lenalidomide maintenance (ClinicalTrials.gov identifier: NCT04497961 ), 19 and a study examining maintenance cessation in patients with sustained MRD negativity (ClinicalTrials.gov identifier: NCT04221178 ). 20

Clinical variables

Patient data were compiled in the MSKCC Myeloma Service database, with key clinical variables including tumor burden (albumin, B2M); organ impairment (particularly renal impairment); ISS, R-ISS, and R2-ISS prognostic scores; MRD status; and progression-free survival (PFS) and overall survival (OS). MRD status was assessed on bone marrow sampling after 4 to 6 cycles of induction therapy and after ASCT by a validated 10-color flow cytometry panel having a sensitivity of 1 × 10 −5 cells. 21 MRD status by Adaptive ClonoSeq was only available for a minority of patients and was therefore not considered for this analysis. PFS was defined by the absence of clinical progression events, changes in therapy for lack of response, or death. OS was defined as the time from treatment start to death from any cause.

Genomic data

Available data included clinical FISH, single-nucleotide polymorphism (SNP) array, and the MSK-IMPACT-Heme (Integrated Mutation Profiling of Actionable Cancer Targets) targeted sequencing panel. 22 , 23 For SNP array or MSK-IMPACT-Heme, DNA was extracted from either bone marrow after CD138 + magnetic bead selection or from plasmacytoma biopsy samples. For MSK-IMPACT-Heme, matched normal controls were collected from nail clippings or saliva. Mutations were detected by hybridization capture of DNA followed by massively parallel sequencing on an Illumina NovaSeq 6000 instrument, whereas copy number calling used the FACETS algorithm. 24

Data were adjusted for tumor purity before applying the 20% cancer clonal fraction (CCF) cutoff for del(17p). Data for allelic status of del(1p32) were not routinely available; therefore, the criterion of IgH translocation together with del(1p32) was considered but not biallelic del(1p32). Amplification (>3 copies) is not considered separately from gain (3 copies) in the CGS , so in this analysis both are annotated as 1q+. Six patients had TP53 status assessed by external next-generation sequencing panels. Patients with data unavailable for TP53 mutational status were included if del(17p) status was available. Patients with data unavailable for 1 of 4 CGS risk criteria were included, whereas those with data missing for >1 criterion were excluded.

Statistical analysis

Kaplan-Meier methods were used for analysis of PFS and OS and to calculate 1-year and 2-year event rates. Differences in PFS and OS between various RFs were assessed in univariable Cox regression models. MRD status was compared between 2 cohorts by Fisher exact test. Multivariate analysis was performed using a Cox proportional hazards model. Visualizations were created using RStudio version 2025.05.0+496.

Results

The cohort included 503 patients treated with either DKRd (n = 149) or DVRd (n = 354), with a median follow-up time of 2.2 years (interquartile range [IQR], 1.3-3.4; maximum, 7.9 years). Among patients treated with DKRd, 42% had follow-up beyond 5 years due to the inclusion of data from the MANHATTAN trial. 18 Patient demographics are presented in Table 1 , with summary clinical outcomes in supplemental Figure 1 . Genomic data were available in 481 of 503 (96%) of patients, with FISH and/or SNP array available in all 481, MSK-IMPACT-Heme in 242, and external next-generation sequencing in 6 patients. TP53 mutation status was available in equal proportions of DKRd-exposed (47%) and DVRd-exposed (50%) patients.

Overall, 31% of cases were defined as HR by at least 1 consensus criterion, higher than when defining by ISS (19%), R-ISS (8%), or R2-ISS (6%), indicating that the IMS-IMWG criteria better delineates risk in patients deemed to be intermediate risk by other prognostic scores. 2 , 8 Overall, 30 of 503 (6%) had insufficient data to classify by the CGS, and if included in the calculation, then 29% overall would be deemed HR. PFS and OS did not differ between DVRd and DKRd ( Figure 1 A, PFS; P = .69; Figure 1 B, OS; P = .8), with patients at HR comprising 27% of DVRd-treated and 34% of DKRd-treated patients, possibly reflecting physician preference for carfilzomib in this real-world cohort given evolving drug efficacy data. 4 , 12 , 25 , 26 Maintenance prescribing patterns evolved during the follow-up period, with lenalidomide alone being the most frequently prescribed overall (36%), followed by lenalidomide with daratumumab (29%; Table 1 ). A total of 40 (8%) patients received daratumumab alone, mainly on clinical trial, 19 and 12 patients ceased maintenance and were monitored without therapy.

ASCT was performed for 52% of patients as part of first-line therapy, with PFS and OS remaining equal between treatment regimens when restricting analysis to patients having received ASCT ( supplemental Figure 2 A, PFS; P = .33; supplemental Figure 2 B, OS; P = .74). There was no significant difference in the proportion of patients undergoing ASCT between the HR and standard-risk (SR) cohorts (54% SR vs 46% HR, P = .13). The median number of induction cycles received before ASCT was 4 (IQR, 4-6), whereas the median number of cycles in total was 6 (IQR, 4-8; Table 1 ). Given the potential variation in cycle number between trial and SOC patients and considering that ASCT is a time-dependent variable, we assessed the impact of the treatment regimen in 2 landmarked survival analyses: PFS after ASCT ( supplemental Figure 3 A; P = .28) and after 1 year of therapy ( supplemental Figure 3 B; P = .68). Because survival outcomes did not differ according to induction regimen prescribed in any of the aforementioned analyses, ongoing assessment of the CGS was done without stratification by treatment.

The distribution of individual HR features within the IMS-IMWG consensus system was 12% of patients with either del(17p) in >20% CCF or TP53 mut, 12% with an IgH translocation (t[ 4;14], t[14;16], or t[14;20]) together with del(1p32) or 1q+, 7% with both del(1p32) and 1q+, and 7% with B2M of ≥5.5 mg/dL and creatinine of <1.2 mg/dL ( Table 1 ). There was a high rate of concurrence of RFs ( Figure 2 A; supplemental Figure 4 ), as previously described in Avet-Loiseau et al. 15 From those with t(4;14), 34 of 47 (72%) also harbored 1q+ or del(1p32), as did 19 of 22 (86%) with t(14;16) and 7 of 7 (100%) with t(14;20).

PFS was significantly inferior in patients with disease categorized as HR by IMS-IMWG criteria, with a median PFS of 2.6 years in the HR group compared with not reached (NR; estimated, >6.8 years) in the SR group ( Figure 2 B, P < .0001). One-year and 2-year PFS in patients at HR were 82% and 63%, respectively, compared with 94% and 88% in those with SR. PFS outcomes by CGS are recapitulated when restricting analysis to patients who underwent ASCT during first-line therapy ( supplemental Figure 5 A; P < .0001), or when performing landmarked analyses either from the time point of ASCT or from 1 year of therapy ( supplemental Figure 5 B-C; each P < .0001). OS, although estimates are immature given a median follow-up time of 2.2 years, was also inferior in patients classified as being at HR by IMS-IMWG criteria ( supplemental Figure 6 A; P = .0016), with the 2-year OS in the HR group being 95% compared with 99% in those with SR. Again, OS outcomes by CGS are recapitulated in landmarked analyses from either the time point of ASCT or from 1 year of therapy ( supplemental Figures 6 B-C).

ISS stage was also predictive of PFS, ( Figure 2 C; P = .00015), with median PFS being NR (>6.4 years), 6.4 years, and 2.9 years for ISS stages I, II, and III, respectively. R2-ISS stage was also predictive of PFS ( supplemental Figure 7 ; P < .0001), with median PFS being NR (>6.8 years), 6.8 years, 3.3 years, and 2.6 years for R2-ISS stages I, II, III, and IV, respectively. Interestingly, from 86 patients noted to be ISS III, 52 (60%) had a creatinine level of ≥1.2 mg/dL and therefore are excluded from risk consideration of tumor bulk (B2M) in the 2025 criteria. However, patients with ISS stage III and renal impairment appear to retain poor prognosis despite treatment with dara-quads, irrespective of CGS status. Although 17 of 52 (33%) of those with high B2M and renal impairment harbored another IMS-IMWG RF, most did not, deeming those patients at HR by ISS but SR by IMS-IMWG criteria. Patients classified as ISS III but SR by CGS (considering all criteria) had a shorter PFS compared with ISS I-II (3.0 years vs NR, >6.4 years, P < .0001; Figure 2 D), and equivalent PFS to those at HR by both ISS and CGS (2.6 years, P value not significant). Again, PFS by ISS is highly analogous when restricting to patients receiving upfront ASCT ( supplemental Figure 8 A-B), with patients classified as ISS III but SR by CGS still having inferior PFS despite ASCT delivery (2.6 years). Therefore, in the context of dara-quads, patients with high B2M and renal impairment, without harboring any other CGS RF, have inferior PFS. This suggests that the IMS-IMWG criteria may potentially be incompletely capturing a small number of patients with high tumor bulk, biologically HR myeloma, which are captured if considering ISS stage regardless of renal function.

Considering the clinical impact of individual CGS criteria in patients treated with dara-quads; PFS was 2.2 years in the presence of TP53 aberration (either del[17p] and/or TP53 mutation), compared with 6.8 years without ( Figure 3 A; P < .0001). Despite affecting a small patient number, the presence of TP53 aberration also had a negative OS impact ( supplemental Figure 9 A; P = .0047). Considering the CCF, PFS was shorter in those with del(17p) present in >20% CCF compared with no 17p aberration ( Figure 3 B; P < .0001), whereas del(17p) present at ≤20% CCF provided the same PFS as with no 17p aberration. Similar results were observed for del(17p) CCF with OS analysis ( supplemental Figure 9 B). Although CCF was routinely reported at MSKCC, 5 patients with del(17p) had their biopsy performed externally, with CCF not being available. In addition, only 14 patients had del(17p) present at ≤20% CCF, and most were censored within 2.5 years, so a larger cohort would be needed to assess the impact of del(17p) CCF with dara-quads. However, findings from this small cohort support the 20% cutoff. Fourteen patients harbored TP53 mutations, with 9 occurring together with del(17p). Although only 1 of the patients with TP53 mutations alone progressed during the available follow-up time, 5 of 9 (56%) of those with biallelic TP53 aberrations progressed within 1.6 years, and 4 of these 5 patients unfortunately died within 3 years of diagnosis.

PFS based on the presence of t(4;14), t(14;16), and 1q+ alone was each significant at a univariate level ( supplemental Figure 10 A-C), with these RF mainly occurring in combination ( Figure 2 A; supplemental Figure 4 ). PFS was 2.0 years in those having 1q+ together with an IgH translocation, 3.6 years in those with 1q+ alone ( P < .0001), and NR (>6.4 years, P = .0005) in the remainder ( Figure 3 C), suggesting that 1q+ alone may retain relevance in the setting of dara-quad therapy. PFS was shorter in patients harboring both del1p and gain1q compared with the remainder of the cohort (2.7 years vs 6.8 years, respectively; Figure 3 D; P = .0068), with survival curve separation occurring after 2 years. In contrast, del(1p32) alone had no impact on PFS, and in combination with IgH translocations was approaching significance ( Figure 3 E; P = .063), although we note low numbers of patients with this combination in the current cohort. Patients with high B2M and normal renal function had a PFS of 2.6 years, whereas those without this criterion had a PFS of 6.8 years ( Figure 3 F; P = .00013). Considering OS, there was a negative OS impact of harboring 1q+ together with an IgH translocation ( supplemental Figure 11 A) and of having high B2M with normal renal function ( supplemental Figure 11 B). With the available follow-up time and patient numbers, neither having del1p with gain1q nor harboring del(1p32) in combination with IgH translocations influenced OS ( supplemental Figure 11 C-D).

Each CGS criterion was significant in multivariate analysis (hazard ratios, 2.0-3.0; supplemental Figure 12 ), aside from the combination of del(1p32) with 1q+ (hazard ratio, 1.5; P = .075). The impact of individual RF retained significance when restricting to patients undergoing upfront ASCT, in those subsets with adequate sample size (aberrant TP53 , IgH translocation with 1q+, and B2M with normal creatinine; supplemental Figure 13 A-F). None of the individual RFs demonstrated a significantly different PFS according to treatment received, nor was there a significant differential treatment effect according to CGS classification ( supplemental Figure 14 ).

Finally, considering the potential use of the IMS-IMWG CGS in clinical trial design as well as eligibility criteria, we examined early MRD results. MRD status after 4 to 6 cycles of induction was assessed in 453 of 503 (90%) patients, with 125 (28%) being MRD-negative (MRDneg) at this early time point, whereas 328 were MRD-positive or had primary refractory disease. There was no significant difference in MRDneg rates after 4 or 6 cycles (79/286 [28%] vs 54/174 [31%]; P = .46), noting that timing of MRD testing was physician-driven based on observed laboratory response. Among patients with CGS HR disease, 32% were MRDneg after 4 to 6 cycles compared with 26% of those with SR ( P = .19), and there was no significant difference in MRDneg achievement after 4 vs 6 cycles according to CGS risk group.

MRD status showed a similar pattern when assessing after first-line ASCT, with 53% of CGS HR being MRDneg compared with 47% of SR ( P = .54). These MRD results are concordant with those observed in clinical trials, particularly the GRIFFIN trial in which MRD was assessed at similar clinical time points. 3 We conclude that CGS HR status was not associated with a lower rate of achieving early MRDneg, despite being predictive of shorter PFS. Currently, given the median follow-up time of 2.2 years, the available data remain immature for prediction of sustained MRDneg. Lastly, considering patients who had achieved MRDneg after ASCT; unfortunately, the CGS remained highly predictive of both PFS and OS when landmarked from either ASCT or 1 year of therapy ( supplemental Figure 15 A-D). Of note, patients at SR who achieved an MRDneg state after ASCT had 100% OS within the available follow-up period. Therefore, CGS status remains highly relevant even in the MRDneg state.

Discussion

The IMS-IMWG CGS was developed from a large data set comprising predominantly clinical trial data, together with the MMRF CoMMpass data set 27 and real-world patient data. The available data, therefore, had the benefit of large numbers, long follow-up, and capacity to test individual RFs in the setting of varied therapeutic drug combinations. The rationale and analysis presented in the recent publication 15 is convincing for the universal adoption of IMS-IMWG CGS for clinical trial eligibility and risk stratification. One limitation of the published data, however, is that historical therapy with long follow-up inevitably contains agents no longer considered best frontline therapies. In addition, given that none of the previous staging systems considered TP53 mutation status and that most centers assess NDMM using FISH rather than sequencing, the number of patients considered for the CGS with TP53 mutation status available was largely from regimens no longer considered as SOC, without TP53 mutation data being available from the seminal trials examining DVRd 3 , 28 and DKRd. 11 , 18

Here, we examined a large single-center cohort of patients treated uniformly with dara-quads, considered the current gold-standard for NDMM induction therapy. 29 We found that 31% of patients would meet criteria for HR by at least 1 CGS criterion, higher than when defining by ISS, R-ISS, or R2-ISS, and higher than described in the consensus definition paper. 15 We note that we may even be underestimating the incidence of patients with disease defined as HR by CGS , given that our available data for TP53 mutations and biallelic del1p32 status is incomplete. Although the R2-ISS score may outperform the CGS at defining ultrahigh risk NDMM ( supplemental Figure 7 ), patients with R2-ISS stage IV here represent 6%, which is impractical for running HR NDMM clinical trials. In contrast, definition of 31% of NDMM as HR strikes the ideal balance of defining HR cases, and being a large enough group to run HR-focused trials. We found evidence of a small group of patients deemed at HR by ISS III but excluded from the CGS due to renal impairment, who appear to retain poor prognosis despite dara-quad therapy ( Figure 2 D). Interestingly, most of these patients did not harbor any of the CGS criteria, suggesting that for a small proportion of patients, ongoing documentation of ISS stage may still be relevant.

We demonstrate a high rate of concurrence of genomic RFs ( Figure 2 A; supplemental Figure 4 ) and confirm that in the setting of highly efficacious therapy, harboring multiple aberrations unfortunately remains adverse ( Figure 3 ; supplemental Figures 7 and 11 ). This finding supports the CGS criteria 15 regarding IgH translocations and chromosome 1 aberrations, and confirms previous studies describing multihit genomic risk affecting survival outcomes. 10 , 11 , 13 , 30 There is significant interest in MRD-guided clinical trial designs, particularly in the context of the recent US Food and Drug Administration approval of MRD as a surrogate trial end point. 31 The MASTER clinical trial provided early evidence that in patients with ≥2 genomic RFs, achievement of early MRDneg was insufficient to prevent cancer progression. 11 Here, we confirm that finding in a large cohort of patients treated with dara-quads, demonstrating equal rates of achievement of early MRDneg in HR and SR, but inferior survival for those at HR, despite ongoing therapy. Specifically, we demonstrate that CGS risk remained predictive of both PFS and OS even in patients MRDneg after ASCT. It is possible that a longer period of intensive therapy and prolonged sustained MRDneg may eventually overcome HR status. Our current data set requires additional follow-up time to interrogate this possibility.

The IMS-IMWG CGS represents, to our knowledge, the first risk definition designed for widespread clinical use in NDMM to include genomic sequencing information rather than being restricted to FISH. This inclusion is supported by noting the dismal outcomes for patients with biallelic TP53 aberrations, even in the face of dara-quad therapy. Targeted sequencing panels such as MSK-IMPACT-Heme 23 are already approved for clinical use, aiding widespread adoption of the CGS. The cohort presented here was treated during a period of evolving genomic testing practice and is therefore incomplete for TP53 mutation assessment and del1p allelic status. However, at our center we now routinely recommend MSK-IMPACT-Heme for all patients with NDMM and anticipate that the CGS will be highly applicable for centers that adopt the recommended guidelines for genomic testing.

Of note, however, is that key genomic features conveying significant prognostic impact in NDMM, (such as apolipoprotein B mRNA editing enzyme, catalytic polypeptide [APOBEC]-mutational activity 17 , 32 , 33 and the complex structural variant chromothripsis 34 , 35 ) are not best defined with targeted sequencing. Whole-genome sequencing (WGS) has been validated for clinical use in other hematologic malignancies, 36 , 37 and if the future aim of the MM community is to provide the most accurate comprehensive assessment of progression risk in NDMM, WGS will no doubt outperform targeted sequencing panels.

In conclusion, our data set of >500 patients treated with dara-quads at MSKCC supports the relevance of the IMS-IMWG CGS and its routine adoption for clinical trial eligibility and design. We note that CGS risk did not predict early MRD status, and that CGS risk remained predictive of PFS and OS even in patients who were MRDneg after ASCT, suggesting that any future MRD-directed treatment decisions should also consider genomic risk. Therefore, we suggest that the IMS-IMWG CGS is an excellent resource for standardizing NDMM trial design, including highly efficacious therapies attempting to improve on dara-quads. In addition, the consensus definition provides important impetus for the immediate clinical integration of targeted sequencing or WGS in NDMM. Any future MRD-directed trial designs may benefit from considering genomic risk together with MRD status, and trial stratification may benefit from considering HR definition by both IMS-IMWG CGS and ISS staging.

Conflict-of-interest disclosure: C.R.T. reports research funding from Takeda and Janssen; and reports honoraria from Sanofi and Janssen. T.S. serves as consultant for Roche-Genentech. H. Hashmi serves as consultant for Amgen, Janssen, and Karyopharm. H. Hassoun reports research funding from Takeda and Janssen. U.A.S. reports research funding to the institution from Bristol Myers Squibb (BMS) and Janssen; and reports honoraria from Janssen and Sanofi. M.H. reports research funding from AbbVie, GSK, SpringWorks Therapeutics, Daiichi Sankyo, and Cosette Pharmaceuticals; and serves as consultant for Curio Science LLC, Intellisphere LLC, Janssen, BMS, and GSK. A.M.L. reports research funding from Pfizer; reports consultancy with, and honoraria from, F. Hoffmann-La Roche Ltd, Janssen, SVB Leerink, and Pfizer; and reports patents and royalties from Serametrix, Inc S.M. reports research funding from BMS, Johnson & Johnson, GSK, and SpringWorks Therapeutics. F.M. serves as consultant for Medidata and Sanofi. M.M. reports advisory board role with, and honoraria and/or research support from, Amgen, BMS, Celgene, Gilead, Janssen, Stemline, SpringWorks, and Takeda. S.G. reports personal fees from, and an advisory role (scientific advisory board) with, Actinium, Celgene, BMS, Sanofi, Amgen, Pfizer, GSK, Jazz, Janssen, Omeros, Takeda, and Kite. G.L.S. reports research funding from Janssen, Amgen, BMS, Beyond Spring; and serves on the data safety monitoring board for ArcellX. M.S. reports research funding from Amgen, Sanofi, Angiocrine Biosciences Inc, and Omeros Corporation; reports consultancy for Miltenyi Biotec and Kite; and reports honoraria from IDEOlogy and MJH Life Sciences (Cancer Network). H.J.L. serves a paid consultant for Takeda, Genzyme, Janssen, Karyopharm, Pfizer, Celgene, and Caelum Biosciences; and has received research support from Takeda. N.K. reports research funding through Amgen; participates in the advisory board with MedImmune and Janssen; reports research funding through Amgen, Janssen, Epizyme, and AbbVie; and consults for Cancer Care Ontario, OncLive, and Intellisphere Remedy Health. S.Z.U. reports grants and personal fees from AbbVie, Amgen, BMS, Celgene, GSK, Janssen, Merck, Mundipharma, Oncopeptides Pharmacyclics, Sanofi, Seattle Genetics, SkylineDX, and Takeda.

Acknowledgments

K.H.M. has received research support from the Multiple Myeloma Research Foundation, the International Myeloma Society, and the American Society of Hematology.

This project was supported by the Memorial Sloan Kettering Cancer Center National Cancer Institute core grant (P30 CA 008748).

Authorship

Contribution: K.H.M. and C.R.T. designed the research, obtained samples, performed research, analyzed data, and wrote the paper; T.S. and C.R. performed research; A.D. analyzed data, and wrote the paper; H. Hashmi, H. Hassoun, U.A.S., M.H., A.M.L., S.M., F.M., M.M., R.F., E.M.J., K.C.M., S.R., S.G., G.L.S., M.S., and H.J.L. obtained samples and wrote the paper; Y.Z., R.C., and M.E.A. performed assays, and wrote the paper; and N.K. and S.Z.U. designed research, obtained samples, performed research, analyzed data, and wrote the paper.

Footnotes

K.H.M. and C.R.T. are joint first authors.

N.K. and S.Z.U. are joint senior authors.

Presented, in poster format, at the annual congress of the European Hematology Association, Milan, Italy, 14 June 2025; and in poster format, at the annual congress of the International Myeloma Society, Toronto, Canada, 19 September 2025.

Deidentified MSK-IMPACT-Heme data are publicly available at www.cbioportal.org .

The full-text version of this article contains a data supplement.

Contributor Information

Kylee H. Maclachlan, Email: maclachk@mskcc.org.

Saad Z. Usmani, Email: usmanis@mskcc.org.

Supplementary Material

Supplemental Figures

References

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