Purpose:

Sustained minimal residual disease (MRD) negativity is associated with long-term survival in multiple myeloma. The gut microbiome is affected by diet, and in turn can modulate host immunity, for example through production of short-chain fatty acids including butyrate. We hypothesized that dietary factors affect the microbiome (abundance of butyrate-producing bacteria or stool butyrate concentration) and may be associated with multiple myeloma outcomes.

Experimental Design:

We examined the relationship of dietary factors (via a food frequency questionnaire), stool metabolites (via gas chromatography–mass spectrometry), and the stool microbiome (via 16S sequencing - α-diversity and relative abundance of butyrate-producing bacteria) with sustained MRD negativity (via flow cytometry at two timepoints 1 year apart) in myeloma patients on lenalidomide maintenance. The Healthy Eating Index 2015 score and flavonoid nutrient values were calculated from the food frequency questionnaire. The Wilcoxon rank sum test was used to evaluate associations with two-sided P < 0.05 considered significant.

Results:

At 3 months, higher stool butyrate concentration (P = 0.037), butyrate producers (P = 0.025), and α-diversity (P = 0.0035) were associated with sustained MRD negativity. Healthier dietary proteins, (from seafood and plants), correlated with butyrate at 3 months (P = 0.009) and sustained MRD negativity (P = 0.05). Consumption of dietary flavonoids, plant nutrients with antioxidant effects, correlated with stool butyrate concentration (anthocyanidins P = 0.01, flavones P = 0.01, and flavanols P = 0.02).

Conclusions:

This is the first study to demonstrate an association between a plant-based dietary pattern, stool butyrate production, and sustained MRD negativity in multiple myeloma, providing rationale to evaluate a prospective dietary intervention.

Translational Relevance

We demonstrate an association between diet, the gut microbiome, and sustained minimal residual disease (MRD) negativity in multiple myeloma. In multiple myeloma on lenalidomide maintenance, stool butyrate concentration at 3 months was associated with higher rates of sustained MRD negativity. Increased seafood and plant proteins, dietary flavonoids, and diversity of dietary flavonoids correlated with stool butyrate concentrations. Thus, a healthy diet, with adequate plant and seafood protein, and containing flavonoids, associates with stool diversity, butyrate production, and sustained MRD negativity. These findings suggest dietary modification should be studied prospectively to enhance myeloma control.

Multiple myeloma remains incurable; however, sustained minimal residual disease (MRD) negativity following therapy represents the best predictor of survival (1). Our prior studies in newly diagnosed multiple myeloma demonstrated an increased relative abundance of Eubacterium hallii and Faecalibacterium prausnitzii in the stool samples collected following induction in MRD-negative patients (2). In allogeneic hematopoietic cell transplantation, an increased abundance of Eubacterium limosum is associated with a lower risk of relapse and prolonged posttransplant survival (3). These bacteria produce the short-chain fatty acid (SCFA) butyrate from dietary fiber and starch in plant foods. The SCFA butyrate can modulate systemic immunity through inhibition of NFκB and histone deacetylases (HDAC), producing transcriptional modulation and a reduction in proinflammatory cytokines (4–6).

Dietary composition plays a significant role in shaping the intestinal microbiome, with enrichment of stool butyrate concentration having been reported in individuals on a plant-based diet compared with an animal-based diet (7). In addition, dietary flavonoids, (plant-derived nutrients) modulate both the microbiome and intestinal immune functions (8–10). We hypothesized that dietary factors that affect the microbiome, in particular the abundance of butyrate-producing bacteria or stool butyrate concentration, may be associated with multiple myeloma outcomes. Here, in the context of lenalidomide maintenance therapy, we evaluated the relationship of dietary factors, stool metabolites, and microbial composition with sustained MRD negativity.

Patients with multiple myeloma eligible for maintenance during first-line therapy were enrolled prospectively, and received lenalidomide for up to 5 years (NCT02538198; ref. 11). The study was conducted in accordance with recognized ethical guidelines (Belmont Report and Declaration of Helsinki) and approved by Memorial Sloan Kettering institutional review board. Written informed consent was obtained from patients. MRD status was evaluated at enrollment then annually using a validated bone marrow–based flow cytometric assay (12) with a sensitivity of at least 10−5. Treatment responses were assessed according to International Myeloma Working Group consensus criteria (13), with sustained MRD negativity defined as MRD negativity at two consecutive time points 1 year apart between enrollment, 12 months, and 24 months. Progression-free survival was not used as an endpoint given the low rate of clinical progression.

Stool samples were collected and analyzed as detailed previously (2) with identification of predicted butyrate-producing bacteria (14). The relative abundance of predicted butyrate-producers and microbiome α-diversity were calculated from 16S microbiome profiles in samples collected 3 months from enrollment. We performed direct quantitation of stool metabolite concentrations using gas chromatography–mass spectrometry on the same stool samples.

Habitual dietary patterns were collected using the Block Food Frequency Questionnaire 2014 (FFQ; ref. 15) and summarized using the United States Department of Agriculture's (USDA) Healthy Eating Index 2015 score (HEI-2015; ref. 16) and a newly developed Dietary Flavonoid Diversity Index (DFDI) by NutritionQuest. The HEI-2015 is a measure of diet quality, assessing 13 nutrient and food group components, with higher scores indicating healthier diets (16). Flavonoid nutrient values were calculated from the FFQ based on USDA data. The DFDI measures the diversity of flavonoid intake from foods and beverages consumed at least once per week, following the Berry–Index method (17, 18); scores range from 0 to 1, with higher scores indicating a greater diversity of flavonoid intake.

Univariate association between sustained MRD negativity and α-diversity (as measured by the inverse Simpson index), relative abundance of butyrate producers, butyrate concentrations, and dietary measurements were assessed using the Wilcoxon rank sum test. Univariate association between autologous hematopoietic cell transplantation (AHCT) status and diversity, butyrate producers, and butyrate concentrations by Wilcoxon rank sum test were also assessed. Multivariable logistic regression analyses between sustained MRD negativity and α-diversity, relative abundance of butyrate producers, butyrate concentrations after adjusting for AHCT status (yes vs. no), age (≤65 or >65 years), gender (male vs. female), and cytogenetics (standard vs. high risk) were assessed. High-risk cytogenetics were defined as presence of either gain 1q21, t(4;14), t(14;16), or deletion 17p. The association between dietary and microbiome data were evaluated by Spearman rank correlation coefficient (R). Logistic regression adjusting for potential confounders [body mass index (BMI), diabetes mellitus (DM), MRD status at enrollment and transplant) was used as a sensitivity analysis for testing the association between sustained MRD negativity and microbiome features. In addition, in exploratory analysis, association between sustained MRD negativity and correlation with diversity, stool butyrate producers, and stool butyrate concentrations were assessed using Spearman rank correlation coefficient. In this exploratory analysis, we declared statistical significance at a two-sided significance level below 0.05. Cytokine analysis (via Olink Target 48 panel) among patients with overlap in butyrate producer, butyrate concentrations, and cytokine data was evaluated using Spearman rank correlation coefficient.

Data availability

The data generated in this study are available upon request from the corresponding author.

Baseline patient characteristics are presented in Table 1 and Supplementary Table S1. Samples were available from 74 patients with multiple myeloma including 59 with assessment of habitual dietary patterns, and 49 with 16S sequencing of the stool microbiome. The overlap of dietary assessment and stool examination was present in 34 patients, of which 32 had stool butyrate concentration measurements (Fig. 1). All patients had MRD status assessed at enrollment, with 42 being MRD positive and 32 MRD negative. Serial assessment of MRD status was performed in 68 patients at 12 months, 61 at 24 months, and 48 at 36 months from enrollment. Sustained MRD negativity was highly correlated with MRD negativity at enrollment and was observed in 32 patients of which 26 were also MRD negative at enrollment.

Table 1.

Patient characteristics by presence or absence of sustained MRD negativity.

Clinical variableSustained MRD negative, n (%)No sustained MRD negative, n (%)P
Patients 32 (100) 36 (100)  
Gender 
 Female 8 (25) 16 (44) 0.13 
 Male 24 (75) 20 (56)  
Age 
 ≤65 years 21 (66) 16 (44) 0.09 
 >65 years 11 (34) 20 (56)  
BMI    
 ≤25 10 (31) 12 (33) 1.0 
 >25 22 (69) 24 (67)  
Diabetes mellitus 
 No 30 (94) 32 (89) 0.68 
 Yes 2 (6) 4 (11)  
High-risk cytogenetics 6 (19) 13 (33) 0.18 
Induction regimen 
 Dara-KRd 2 (6) 2 (6) 1.0 
 KRd 21 (66) 18 (50) 0.23 
 VRd 7 (22) 10 (27) 0.78 
 CyBorD 2 (6) 0.49 
 Other 2 (6) 4 (11) 0.68 
Posttransplant status 
 Yes 14 (44) 15 (42) 1.0 
 No 18 (56) 21 (58)  
MRD negativity at enrollment 
 Yes 25 (78) 6 (17) 0.0001 
 No 7 (22) 30 (83)  
Clinical variableSustained MRD negative, n (%)No sustained MRD negative, n (%)P
Patients 32 (100) 36 (100)  
Gender 
 Female 8 (25) 16 (44) 0.13 
 Male 24 (75) 20 (56)  
Age 
 ≤65 years 21 (66) 16 (44) 0.09 
 >65 years 11 (34) 20 (56)  
BMI    
 ≤25 10 (31) 12 (33) 1.0 
 >25 22 (69) 24 (67)  
Diabetes mellitus 
 No 30 (94) 32 (89) 0.68 
 Yes 2 (6) 4 (11)  
High-risk cytogenetics 6 (19) 13 (33) 0.18 
Induction regimen 
 Dara-KRd 2 (6) 2 (6) 1.0 
 KRd 21 (66) 18 (50) 0.23 
 VRd 7 (22) 10 (27) 0.78 
 CyBorD 2 (6) 0.49 
 Other 2 (6) 4 (11) 0.68 
Posttransplant status 
 Yes 14 (44) 15 (42) 1.0 
 No 18 (56) 21 (58)  
MRD negativity at enrollment 
 Yes 25 (78) 6 (17) 0.0001 
 No 7 (22) 30 (83)  

Note: High-risk cytogenetics % from 68 available results MRD at 12 months. % from 63 available samples.

Abbreviations: CyBorD, cyclophosphamide, bortezomib, dexamethasone; Dara-KRd, daratumumab, carfilzomib, lenalidomide, dexamethasone; KCyD, carfilzomib, cyclophosphamide, dexamethasone. Other includes Vd, KCyD, thal; KRd, carfilzomib, lenalidomide, dexamethasone; Vd, bortezomib, dexamethasone; VRd, bortezomib, lenalidomide, dexamethasone.

Figure 1.

Sample description. A, CONSORT diagram demonstrating availability of dietary, microbiome, and metabolite data. B, Venn diagram demonstrating the overlap between the three methods of assessment.

Figure 1.

Sample description. A, CONSORT diagram demonstrating availability of dietary, microbiome, and metabolite data. B, Venn diagram demonstrating the overlap between the three methods of assessment.

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As a subset of patients had undergone AHCT prior to maintenance therapy (33/74, 45%), the timepoint for microbiome evaluation of 3 months post-enrollment was chosen to allow resolution of post-AHCT reduction in microbiome diversity (19). α-diversity of the fecal microbiome at 3 months was significantly higher in those with sustained MRD negativity [median 16.9, interquartile range (IQR) 14.8–28.0], compared with those without (median 11.9, IQR 9.9–18.6; P = 0.0035; Fig. 2A). The relative abundance of predicted butyrate-producers was also significantly higher in patients with sustained MRD negativity (median 0.093, IQR 0.072–0.100) than those without (median 0.054, IQR 0.035–0.094; P = 0.025; Fig. 2B).

Figure 2.

Stool α-diversity, abundance of butyrate producers, and butyrate concentration associate with MRD negativity. A, Stool α-diversity at 3 months by inverse Simpson index according to sustained MRD status. B, Relative abundance of butyrate producers at 3 months according to sustained MRD status. C, Concentration of stool butyrate at 3 months according to sustained MRD status (achievement of sustained MRD negativity).

Figure 2.

Stool α-diversity, abundance of butyrate producers, and butyrate concentration associate with MRD negativity. A, Stool α-diversity at 3 months by inverse Simpson index according to sustained MRD status. B, Relative abundance of butyrate producers at 3 months according to sustained MRD status. C, Concentration of stool butyrate at 3 months according to sustained MRD status (achievement of sustained MRD negativity).

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Stool butyrate concentration at 3 months was significantly higher in patients who achieved sustained MRD negativity (median 18.1, IQR 10.7–29.0 mmol/L) compared with those who did not (median 10.0 mmol/L, IQR 6.6–16.2 mmol/L; P = 0.037; Fig. 2C). In addition, AHCT status was not associated with diversity (P = 0.82), relative abundance of butyrate producers (P = 0.44), and stool butyrate concentrations (P = 0.99) at 3 months. On multivariate analysis after adjusting for AHCT status, age, gender, cytogenetics, stool microbiome α-diversity (P = 0.004) and relative abundance of butyrate producers (P = 0.03) at 3 months retained significance for association with sustained MRD negativity (Supplementary Table S2). Other stool metabolites (acetate, propanoate, valerate, heptanoate, isobutyrate, methylbutyrate, isovalerate) did not correlate with MRD status (P values >0.1). We saw weak positive correlations between plasma CCL2 and IL33 with butyrate levels (R2 values 0.17 and 0.19, respectively). We evaluated antibiotic use within 2 months of a sample being collected, and our findings were independent of antibiotic use. Collectively, these data suggest that sustained MRD negativity in multiple myeloma is associated with higher α-diversity, relative abundance of butyrate producers, and concentration of stool butyrate.

Considering that dietary factors play an important role in the production of SCFA, we examined the relationship between diet composition (components of HEI-2015 score), stool butyrate concentration, and subsequent MRD status and identified certain significant correlations (Supplementary Table S3). The components total protein as well as seafood and plant protein were associated with stool butyrate concentration at 3 months (R = 0.5, P = 0.004 and R = 0.45, P = 0.009 respectively). Seafood and plant proteins include seafood, nuts, seeds, soy products (excluding beverages), and legumes (beans and peas). The standard for maximum score is ≥0.8 cup equivalent per 1,000 kcal and standard for minimum score of zero is no seafood or plant proteins. These components also correlated with sustained MRD negativity (P = 0.01 and P = 0.05, respectively). To further explore the evidence supporting a plant-based diet, we measured dietary flavonoids in this cohort. Total anthocyanidins (R = 0.47, P = 0.01), flavones (R = 0.48, P = 0.01), and flavanols (R = 0.42, P = 0.02) correlated with stool butyrate. In addition, the Dietary Flavonoid Diversity Index was associated with butyrate concentration (R = 0.46, P = 0.008) and microbiome diversity (R = 0.38, P = 0.03; Supplementary Table S4).

In the context of lenalidomide maintenance therapy for multiple myeloma, we demonstrate for the first time an association between diet, the gut microbiome, and sustained MRD negativity in multiple myeloma. Our data show that sustained MRD negativity among patients receiving lenalidomide is associated with higher microbial α-diversity, relative abundance of butyrate producers, and concentration of stool butyrate measured after 3 months on lenalidomide maintenance. Together with our prior publication demonstrating increased butyrate producers, specifically Eubacterium hallii and Faecalibacterium prausnitzii in MRD-negative patients following induction therapy (2), this study further strengthens the hypothesis that specific microbiome features, especially butyrate concentrations, may predict clinical outcomes in multiple myeloma.

Butyrates have previously been shown to modulate immunity by exerting anti-inflammatory functions through inhibition of the transcription factor NFκB, leading to reduced formation of proinflammatory cytokines (4, 5). Butyrates also noncompetitively inhibit HDACs, acting in the same way as panobinostat, an HDAC inhibitor with activity in multiple myeloma (4, 6). In this study, relationships between serum cytokine values and microbial features were not robust and may be due to sample size or assay sensitivity warranting larger sample size in future studies.

The dietary associations described in our study are consistent with prior epidemiologic data. Higher HEI-2015 scores correlated with reduced cancer risk and mortality (20, 21). In the EPIC Oxford study, including 61,647 individuals of which 65 developed multiple myeloma, those on vegetarian and vegan diets had reduced risk of development of multiple myeloma compared with meat eaters [relative risk, 0.23; 95% confidence interval (CI), 0.09–0.59; ref. 22]. In the Nurses’ Health Study and Health Professionals Follow-up study that included 165,796 individuals with 423 multiple myeloma cases and 345 deaths, those with healthier prediagnosis dietary pattern based on the alternative healthy eating index 2010 had lower multiple myeloma mortality (HR, 0.76; 95% CI, 0.67–0.87; ref. 23). The association of butyrate concentration with seafood and plant protein scores, dietary flavonoids, and dietary flavonoid diversity support the hypothesis that a diverse plant-based diet may have an impact on multiple myeloma via butyrate production suggesting the potential for an underlying mechanistic basis (10).

Strengths of this study include the availability of simultaneous dietary, stool microbiome, and metabolite assessment, as well as long-term MRD data, and the consistent association between the relative abundance of predicted butyrate producers and stool butyrate concentrations. Limitations include the lack of untreated patient samples prior to myeloma therapy initiation and a small sample size that had all study assessments. This may limit sensitivity to assess potential confounders related to an individual's lifestyle and circumstances at time of sample acquisition, although importantly our study did exclude subjects on continuous systemic immunosuppressive therapy and patients with gastrointestinal conditions that would impair absorption of lenalidomide. Additional limitations include dietary recall bias and utilization of flow-based MRD testing with a sensitivity of 10−5, which may be discordant with assays having sensitivity of 10−6. Future comprehensive studies evaluating plasma and bone marrow cytokine levels may shed additional light on the anti-inflammatory effects of butyrate when correlated with its stool and plasma concentrations.

Nevertheless, our data support the hypothesis that a healthy diet, with adequate high-quality plant and seafood protein, and dietary flavonoids may have a positive impact on stool diversity, butyrate production, and multiple myeloma outcomes (Fig. 3).

Figure 3.

Schema for the hypothesis on how dietary composition may impact the intestinal microbiome and sustained MRD negativity. (Created with BioRender.com.)

Figure 3.

Schema for the hypothesis on how dietary composition may impact the intestinal microbiome and sustained MRD negativity. (Created with BioRender.com.)

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In conclusion, this is the first study to show that dietary and microbiome factors may be associated with sustained MRD negativity in plasma cell disorders. Our study suggests that lifestyle modification in the form of dietary change may potentially contribute to multiple myeloma control. We therefore believe that further study of prospective dietary interventions in plasma cell disorders is warranted and have initiated a whole-foods plant-based dietary interventional trial in multiple myeloma precursor disease (NCT04920084, NUTRIVENTION).

U.A. Shah reports grants from NIH/NCI Cancer Center Support Grant P30 CA008748, MSK Paul Calabresi Career Development Award for Clinical Oncology K12CA184746, Paula and Rodger Riney Foundation, and Allen Foundation Inc, as well as non-financial support from American Society of Hematology Clinical Research Training Institute and NutritionQuest during the conduct of the study. U.A. Shah also reports grants from Parker Institute for Cancer Immunotherapy at MSK, HealthTree Foundation, and International Myeloma Society; other support from Celgene/BMS and Janssen; personal fees from ACCC, MashUp MD, Janssen Biotech, Sanofi, BMS, MJH LifeSciences, Intellisphere, Phillips Gilmore Oncology Communications, and RedMedEd; and non-financial support from TREC Training Workshop R25CA203650 (PI: Melinda Irwin) outside the submitted work. P. Adintori reports other support from Vidafuel, Inc. and other support from Fig Health, Inc. outside the submitted work. M.J. Pianko reports grants and personal fees from Pfizer and Sanofi; grants from Nektar, Regeneron, Bristol Myers Squibb, and AbbVie; and personal fees from Janssen Biotech, Karyopharm, and Oncopeptides outside the submitted work. S. Mailankody reports other support from Takeda Oncology, Janssen Oncology, Bristol Myers Squibb, Allogene Therapeutics, and Fate Therapeutics, as well as personal fees from PleXus Education, Physician Education Resource, Evicore, Legend Biotech, Cancer Network, Janssen Oncology, BioAscend, Optum Health, and eCOR1 outside the submitted work. N. Korde reports other support from Amgen and Janssen, as well as personal fees from Medimmune, CCO, PER, Cancer Network, Onc Live, and Intellisphere outside the submitted work. M. Hultcrantz reports grants from GSK, Amgen, and Daiichi Sankyo, as well as other support from BMS and Intellisphere LLC outside the submitted work. C.R. Tan reports grants and personal fees from Janssen, as well as personal fees from MJH Life Sciences outside the submitted work. B. Diamond reports personal fees from Janssen, Sanofi, and Medscape outside the submitted work. G. Shah reports other support from Janssen, Amgen, and Beyond Spring outside the submitted work. M. Scordo reports personal fees from McKinsey & Company, Omeros Corporation, Amgen, Kite – A Gilead Company, and i3Health; personal fees and other support from Angiocrine Bioscience, Inc.; and other support from Medscape, LLC outside the submitted work. H. Landau reports grants and personal fees from Takeda, Janssen, and Caelum Biosciences, as well as personal fees from Pfizer and Genzyme outside the submitted work. S.Z. Usmani reports grants and personal fees from Amgen, AbbVie, BMS/Celgene, GSK, Janssen, Merck, Sanofi, Seattle Genetics, SkylineDX, Takeda, and TeneoBio; grants from Array Biopharma and Pharmacyclics; and personal fees from SecuraBio, Oncopeptides, Gilead, and Genentech outside the submitted work. S. Giralt reports other support from Amgen, Actinium, Celgene, Johnson & Johnson, Miltenyi Biotec, Takeda, Sanofi, Kite Pharmaceuticals, Celgene Corp, Novartis, and Jazz outside the submitted work. C.O. Landgren reports grants and personal fees from Amgen and Janssen; grants from Celgene, Tow Foundation, Perelman Family Foundation, National Cancer Institute, and Riney Foundation; other support from Takeda, Janssen, and Merck; and personal fees from Karyopharm, Adaptive Biotech, Binding Site, and Bristol Myers Squibb outside the submitted work. T. Block reports other support from MSKCC during the conduct of the study. J.U. Peled reports grants from NCI P30 CA008748, NHLBI K08HL 143189, and Parker Institute for Cancer Immunotherapy at Memorial Sloan Kettering during the conduct of the study. J.U. Peled also reports personal fees, non-financial support, and other support from Seres Therapeutics; personal fees from DaVolterra, CSL Behring, and MaaT Pharma; and personal fees and other support from Postbiotics Research outside the submitted work. In addition, J.U. Peled has a patent for Methods and compositions for detecting risk of cancer relapse pending and licensed to Seres Therapeutics, a patent for Bacterial compositions and methods for cancer survival pending and licensed, and a patent for Beta-lactamase compositions for treatment of graft versus host disease pending; Memorial Sloan Kettering Cancer Center (MSK) has financial interests relative to Seres Therapeutics. A.M. Lesokhin reports grants from Bristol Myers Squibb and Genentech; grants, personal fees, and non-financial support from Pfizer; and grants and personal fees from Janssen outside the submitted work; in addition, A.M. Lesokhin has a patent for US20150037346A1 licensed and with royalties paid from Serametrix, Inc. No disclosures were reported by the other authors.

U.A. Shah: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, investigation, writing–original draft, writing–review and editing. K.H. Maclachlan: Data curation, formal analysis, investigation, writing–original draft, writing–review and editing. A. Derkach: Data curation, software, formal analysis, visualization. M. Salcedo: Data curation. K. Barnett: Data curation. J. Caple: Data curation. J. Blaslov: Data curation. L. Tran: Data curation. A. Ciardiello: Data curation. M. Burge: Data curation. T. Shekarkhand: Data curation. P. Adintori: Data curation, investigation. J. Cross: Formal analysis, investigation, methodology. M.J. Pianko: Data curation, investigation, writing–original draft. K. Hosszu: Investigation, methodology. D. McAvoy: Investigation, methodology. S. Mailankody: Data curation, investigation, writing–original draft. N. Korde: Data curation, investigation, writing–original draft. M. Hultcrantz: Data curation, investigation, writing–original draft. H. Hassoun: Data curation, investigation, writing–original draft. C. Tan: Data curation, investigation, writing–original draft. S.X. Lu: Data curation, investigation. D. Patel: Data curation, investigation, writing–original draft. B. Diamond: Data curation, investigation, writing–original draft. G. Shah: Data curation, investigation, writing–original draft. M. Scordo: Data curation, investigation, writing–original draft. O. Lahoud: Data curation, investigation, writing–original draft. D.J. Chung: Data curation, investigation, writing–original draft. H. Landau: Data curation, investigation, writing–original draft. S.Z. Usmani: Data curation, investigation, writing–original draft. S. Giralt: Data curation, investigation, writing–original draft. Y. Taur: Formal analysis, methodology. C.O. Landgren: Conceptualization, resources, data curation, investigation, methodology, writing–original draft. G. Block: Formal analysis, investigation. T. Block: Formal analysis, investigation. J.U. Peled: Formal analysis, investigation, methodology, writing–original draft. M.R.M. van den Brink: Formal analysis, investigation, methodology, writing–original draft. A.M. Lesokhin: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing.

This study is funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748, Sawiris Family Fund, Paula and Rodger Riney Foundation, and Celgene. U.A. Shah received research support from the American Society of Hematology Clinical Research Training Institute, Parker Institute for Cancer Immunotherapy, International Myeloma Society, Paula and Rodger Riney Foundation, TREC Training Workshop R25CA203650 (PI: Melinda Irwin), NCI MSK Paul Calabresi Career Development Award for Clinical Oncology K12 CA184746, HealthTree Foundation, and the Allen Foundation Inc. K.H. Maclachlan received research support from a Multiple Myeloma Research Foundation Fellow Award, an American Society of Hematology Restart Research Award, and a Royal Australasian College of Physicians Research Establishment Grant. M.J. Pianko received research support from NIH grants (National Institute of Aging: P30-AG024824; NCI: P30-CA046592, and the National Center for Advancing Translational Sciences award UL1-TR00457). A.M. Lesokhin received research support from NCI 1R01CA249981–01, Sawiris Family Fund, and Paula and Rodger Riney Foundation. J.U. Peled reports funding from NHLBI NIH Award K08HL143189, the MSKCC Cancer Center Core Grant NCI P30 CA008748. This research was supported by the Parker Institute for Cancer Immunotherapy at Memorial Sloan Kettering Cancer Center; J.U. Peled is a member of the Parker Institute for Cancer Immunotherapy. M.R.M. van den Brink was supported by National Cancer Institute award numbers, R01-CA228358, R01-CA228308, and P01-CA023766; National Heart, Lung, and Blood Institute (NHLBI) award number R01-HL123340 and R01-HL147584; National Institute of Aging award number P01-AG052359; Starr Cancer Consortium; and Tri Institutional Stem Cell Initiative. Additional funding was received from The Lymphoma Foundation, The Susan and Peter Solomon Divisional Genomics Program, Cycle for Survival, and the Parker Institute for Cancer Immunotherapy.

The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).

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