Mulligan, George

Abstract Multiple myeloma is a malignancy of clonally expanded plasma cells shaped by complex interactions with the immune microenvironment (IME). To investigate immune correlates of treatment response and disease progression, we conducted multi-omics profiling including CD138neg single-cell RNA sequencing of 243 bone marrow samples from 102 patients (631,226 cells) and CD138pos bulk RNA and whole-genome sequencing from 209 samples. In longitudinal analyses, interferon-γ signaling associated with markers of impaired T-cell memory after autologous stem cell transplant, whereas naïve B-cell abundance and immunoglobulin diversity correlated with improved progression-free survival (HR = 0.48; P = 2.3e−4). At disease progression, multiple myeloma cells upregulated cancer–testis antigens (CTAg) and immune effector genes, with concurrent B-cell depletion, enrichment of myeloid-derived suppressor cell expression, and phenotypic T-cell exhaustion. These findings highlight dynamic immune–tumor interactions, identifying naïve B-cell reconstitution as a biomarker of durable response and CTAgs as potential targets for high-risk disease at progression. Significance: Longitudinal profiling of multiple myeloma and the IME revealed dynamic immune–tumor interactions across the disease course. Altered expression in CD8+ T cells limited memory phenotype after transplant, whereas naïve B-cell recovery associated with sustained treatment response. At progression, CTAg expression associated with immunosuppression, revealing novel mechanisms of immune dysregulation.

Abstract Introduction Understanding the tumor immune microenvironment (TIME) in multiple myeloma (MM) is critical for identifying prognostic biomarkers and therapeutic targets. We hypothesized that specific immune cell populations within the bone marrow TIME are associated with clinical outcomes and could enhance patient stratification. To this end, we conducted the largest CyTOF-based immune profiling study to date in MM, profiling >300 million bone marrow immune cells from patients enrolled in the CoMMpass study. Methods We analyzed >1,000 bone marrow samples from ~700 newly diagnosed MM patients enrolled in the MMRF CoMMpass study (NCT01454297), using a harmonized protocol and 41-antibody CyTOF panel at five academic centers. The majority were baseline samples (n=671), with additional samples spanning up to 3.5 years post-diagnosis (n>300). Each site contributed ~200 samples on average. The cohort included 40% females, with a median age of 63 years and BMI of 27.8. ISS stages were evenly distributed (I: 36%, II: 36%, III: 29%), and 84% had ECOG 0-1. Autologous stem cell transplant was performed in 58% of patients, and 22% received maintenance therapy. Results We profiled 311 million single cells across the cohort, identifying major immune compartments: Neutrophils (36.0%), CD4 T cells (19.7%), NK cells (9.9%), monocytes (7.6%), B cells (5.7%), and CD8 T cells (5.6%). Less abundant populations included dendritic cells, γδ T cells, plasmablasts, and pDCs. In our preliminary analysis with multivariate cox regression modeling, we identified clinical factors including stem cell transplant, ISS stage, and ECOG status, as independent predictors of overall survival (OS). After adjusting for age and sex, higher proportions of naïve B cells, pDCs, and γδ T cells were independently associated with improved OS, while CD4+ T cell abundance was positively associated with prolonged progression-free survival (PFS). In contrast, elevated neutrophil proportions were significantly linked to inferior OS and demonstrated a trend toward shorter PFS. Longitudinal immune profiling in more than 200 patients identified a gradual accumulation of naïve B cells and a pronounced post-treatment rise in CD45+ plasmablasts, highlighting persistent B cell activity as a candidate mechanism for sustained therapeutic efficacy. Conclusion This study presents the largest immune proteomic atlas in MM to date, linking TIME composition to clinical outcomes. A favorable immune landscape was marked by preserved humoral immunity (naïve B cells, plasmablast expansion), enrichment of pDCs and γδ T cells, and lower neutrophil abundance, collectively associated with improved survival outcomes. These findings lay the groundwork for immune-guided prognostication and highlight new opportunities for therapeutic targeting in MM. Ongoing analyses aim to further resolve baseline and longitudinal immune dynamics in relation to treatment response.

Abstract Multiple myeloma (MM) is an incurable malignancy of clonally expanded plasma cells shaped by complex interactions with the immune microenvironment. To investigate immune factors driving treatment response and resistance, we conducted multi-omics profiling including CD138 neg single-cell RNA sequencing of 243 bone marrow samples from 102 patients (631,226 cells) and CD138 pos bulk RNA and whole-genome sequencing from 209 samples. Longitudinal analyses revealed that interferon gamma signaling impairs T cell memory after autologous stem cell transplant, while naïve B cell abundance and immunoglobulin diversity correlated with improved progression-free survival (HR = 0.48, p = 2.3e -4 ). At disease progression, MM cells upregulated cancer-testis antigens and immune effector genes, with concurrent B cell depletion, enrichment of myeloid-derived suppressor cell genes in monocytes, and T cell exhaustion. These findings highlight dynamic immune-tumor interactions, identifying naïve B cell reconstitution as a biomarker of durable response, and cancer-testis antigens as potential targets for high-risk disease at progression. Statement of Significance Longitudinal profiling of multiple myeloma and the immune microenvironment revealed dynamic immune-tumor interactions across the disease course. Dysfunctional CD8⁺ T cells limited memory formation post-transplant, while naïve B recovery associated with sustained treatment response. At progression, cancer-testis antigen expression associated with immunosuppression, revealing novel mechanisms of immune escape.

Introduction: In recent years, multiple myeloma (MM) research has increasingly focused on unraveling the complex interactions between malignant plasma cells and their surrounding bone marrow microenvironment (BMME). To further elucidate these interactions, we developed a comprehensive single-cell atlas of the MM BMME, integrating both coding and noncoding (ncRNA) transcriptional profiles. By correlating these profiles with specific cytogenetic abnormalities in myeloma cells, we aim to uncover how ncRNAs regulate the phenotypic and functional states of myeloma cells and, consequently, shape the cellular landscape of the BMME. A detailed understanding of how the noncoding transcriptome affects the cellular components and immune landscape of the BMME is essential for advancing our knowledge of MM pathogenesis and identifying novel biomarkers and therapeutic targets. Methods: Our atlas was constructed using single-cell RNA sequencing data from 481 samples of CD138neg cells sorted from bone marrow aspirates of MM patients enrolled in the MMRF CoMMpass study. This dataset includes accompanying whole-genome sequencing and survival data, enabling correlative analysis between transcriptional profiles, cytogenetic abnormalities, and patient outcomes. To generate a combined coding and non-coding transcriptome, we developed an expanded human reference genome by systematically merging mRNA and ncRNA transcripts from the LncBook2.0 and GENECODEv42 reference genomes. The sequencing data was aligned to this expanded genome, followed by quality control processing, batch correction, clustering, and supervised analysis. Results: The resulting coding and non-coding atlas comprises over 1.9 million cells, spanning immune, plasma, and stromal compartments. Remarkably, ncRNAs constituted 46% of the associated genes, including 22,102 long ncRNAs (lncRNAs) and 3,112 small ncRNAs. Subclustering analysis of the major immune compartments (B lymphoid, myeloid, and NK and T lymphoid) yielded 70 subclusters, with ncRNAs representing 24-38% of the top 100 differentially expressed genes within each compartment. This underscores the significant role of ncRNAs in distinguishing immune subpopulations. Moreover, examining the association between specific cytogenetic abnormalities and the immune composition of the BMME, 30 subclusters were found to be differentially abundant (P < 0.05) between cytogenetic abnormalities, supporting our hypothesis that myeloma cell genotypes significantly shape the immune microenvironment. For instance, patients with CCND1 amplification displayed an increase CD4+ central memory T cells with a decrease in CD8+ cytotoxic T cells. Similarly, patients with 1q21 gain exhibited an increase in TGFβ-stimulated monocytes, while a decrease in cytotoxic CD8+ T and CD56dim NK cells was observed in patients with 17p13 deletion. Next, examining which ncRNAs are associated with specific cytogenetic abnormalities, we identified 14 ncRNAs significantly differentially expressed across cytogenetic abnormalities. Increased expression of six of these ncRNAs were associated with poor overall survival both independently and in combination (P < 0.05, HR = 3.7). Notably, three of these ncRNAs have documented roles in cancer progression but have not been previously studied in the context of MM. Subsequently stratifying patients based on their enrichment of these outcome-associated ncRNAs revealed even stronger associations with immune composition, including an increase in CD4+ regulatory T cells (P < 0.05). This further supports our hypothesis that myeloma cell genotypes shape the BMME. Lastly, by constructing a gene regulatory network to predict the interactions between coding and noncoding RNAs, we illustrate the regulatory networks of ncRNAs modulating the phenotypes of myeloma and immune cell subpopulations. Conclusion: In summary, we present the first high-resolution transcriptomic atlas of the MM BMME, integrating both coding and noncoding RNAs. This study reveals distinct immune subpopulations associated with specific myeloma cell genotypes and highlights the central role of ncRNAs in modulating the phenotypic and functional states of both malignant and non-malignant cells in the MM BMME. Our findings emphasize the potential of ncRNAs as therapeutic targets and prognostic markers for MM, offering new avenues for research and clinical intervention.

Abstract Introduction: We investigated tumor gene expression profiling (PGx) to identify predictive classifiers of response and elucidate the mechanism of efficacy of proteasome inhibition in multiple myeloma (MM) by bortezomib, a first-in-class proteasome inhibitor. Analysis of 44 samples from the SUMMIT phase 2 trial identified predictive classifiers of response in patients (pts) with refractory MM (ASH 2002, Abstr 1519). We now report on the PGx analysis of the SUMMIT-derived classifier in an independent sample set from the APEX phase 3 trial, a multicenter randomized study of 669 pts that demonstrated the superiority of bortezomib to high-dose dex for relapsed MM following 1 to 3 prior therapies (Richardson NEJM 2005). As well, we report on a new classifier of response built by combining samples from SUMMIT and MLN34101-040 (040) (a study of bortezomib in pts who failed dex in APEX or were not eligible for APEX) and tested in the APEX samples. Methods: 93 participating clinical centers collected pretreatment bone marrow aspirates and enriched tumor cells on site before centralized RNA extraction and microarray analysis. Approximately 85% of pts consented to whole-genome analysis. Quality control for RNA integrity and quantity, microarray data quality, and tumor cell purity resulted in 129 patient samples from APEX evaluable for PGx; 67 were from the bortezomib arm and 62 from the dex arm. 48 samples were evaluable from 040. Patient characteristics of this subset were similar to those of the overall study population. Predictive classifiers were identified and tested via separate training and test datasets. Results: Consistent with the SUMMIT analysis, the APEX and 040 datasets exhibit biologic features expected of MM, including the overexpression of translocation target genes such as cyclin D1, MAF, and MMSET/FGFR3. Classifiers built using only the SUMMIT samples predict the outcome of pts in the APEX bortezomib arm with an overall accuracy of 51% (sensitivity [Sn] = 49%, specificity [Sp] = 55%; P = .81) and in the dex arm with an overall accuracy of 56% (Sn = 64%, Sp = 50%; P = .31). If the classifier was built using data from both the SUMMIT and 040 trials (87 total samples), the prediction of response in the APEX bortezomib arm was statistically significant (66% accuracy, Sn = 89%, Sp = 42%; P = .006). The same classifier only predicts the response of dex-treated pts with an overall accuracy of 46% (Sn = 72%, Sp = 28%; P = .88), suggesting specificity for bortezomib. Many of the overexpressed genes associated with bortezomib insensitivity relate to protein synthesis (EIF4B, RPL5) and RNA binding (SFRS10, CPSF6). Future Directions: This work highlights practical and scientific issues that inform future efforts to implement PGx in clinical trials. Predictive accuracy may be influenced by the clinical end point, limited sample size, and/or technical considerations associated with distinct trials and genomic data. The influence of study differences (relating to pts’ time from diagnosis, number of prior therapies, molecular subtypes, or hybridization characteristics) on the ability to build classifiers and identify biologic pathways related to response are being examined; these and potential insights on drug mechanisms will be presented.

Abstract The biological heterogeneity of myeloma can be largely explained by genetic subtypes characterized by primary translocations (t(4;14), t(11;14), and t(14;16)) or trisomies of odd-numbered chromosomes (hyperdiploid myeloma). In newly diagnosed patients, it is well established that these genetic subtypes have different survival. However, the prevalence and survival of the main genetic subtypes in relapse patients is unknown. In this study, we examined the prevalence and survival according to TC classes (a gene expression-based classification system which identifies the main genetic subtypes of myeloma) in patients with relapsed myeloma entered into bortezomib trials who had gene expression profiling (GEP) performed as part of a pharmacogenomics study (Bortezomib cohort). In comparison, we analyzed a large publicly available GEP dataset of newly diagnosed patients treated with total therapy II from the University of Arkansas for Medical Science (UAMS cohort, GEP data available on Gene Expression Omnibus, Zhan et al Blood 2006 epub). GEP was performed using the U133A and B chip for the bortezomib cohort and U133plus chip for the UAMS cohorts (Affymetrix, Santa Clara, CA). For both dataset, raw gene expression were log-transformed, median centered and analyzed using Genespring 7.0 (Agilent Technologies, Palo Alto, CA). A proliferation index (PI) based on the expression of 7 genes (TYMS, TOP2A, CCNB1, KIAA0101, CKS1B, UBE2C, TRIP13) was also calculated. As shown previously (Mulligan et al ASH 2005 abstract) the prevalence of the different TC classes in relapse patients was very similar to newly diagnosed patients (Chi-square p=0.25). In the bortezomib cohort, patients with t(11;14) had the worst survival when compared to patients with t(4;14), maf translocation and hyperdiploidy who had similar survival (survival of t(11;14) versus others, log-rank p=0.036). In contrast, among newly diagnosed patients (UAMS cohort) those with t(4;14) and maf translocations had the worst prognosis, whereas t(11;14) and hyperdiploid patients had the best prognosis (log-rank p=0.02). This interesting contrast in the prognosis within these main genetic subtypes of myeloma at diagnosis and relapse suggest that at relapse, the survival of these patients may be modulated by secondary events or possibly the treatment received. Indeed, a greater proportion of relapsed t(11;14) have a high PI (Chi-square p = 0.04) than newly diagnosed t(11;14). In contrast, there is no difference between the PI of newly diagnosed and relapsed hyperdiploid MM (TC D1, p=0.19). Our observations suggest that although the composition of the various genetic subtypes at diagnosis and relapse appear similar, the prognosis of some subtypes (especially t(11;14)) can be markedly different at relapse when modulated by secondary events (e.g. increase proliferation) whereas others (such as hyperdiploid MM) appear to behave similarly at diagnosis and relapse. Our observation also has important therapeutic implications as it suggests relapsed t(11;14) should be treated aggressively and drug combinations or addition of new agents may be more appropriate. Conversely, bortezomib seemed to be an effective therapy in relapse t(4;14) patients, improving the prognosis of these patients to that of genetic subgroups with good prognosis. Our study highlights the fact that different treatment strategies may be needed for newly diagnosed and relapsed patients even within same genetic subtypes.

Abstract Introduction: Clinical development of bortezomib in multiple myeloma (MM) included pharmacogenomic gene expression profiling (GEP) of pre-treatment tumor biopsies in order to evaluate the feasibility of developing predictive classifiers in prospective clinical trials and to better define the biological pathways related to drug sensitivity. By partitioning the samples into train (n=101) and test (n=80) datasets, statistically significant classifiers of response and survival were identified and then validated [Mulligan G, et al: Blood. 2007; 109:3177–88]. Here we describe specific genes and biological pathways associated with bortezomib efficacy in relapsed MM. Methods: Using tumor GEP data from 188 patients treated with single-agent bortezomib we identified genes associated with overall survival (OS), as well as genes associated with response (R) or progressive disease (PD) after bortezomib therapy. These clinical endpoints were also analyzed with biological pathway algorithms including GSEA [Subramanian A, et al: Proc Natl Acad Sci2005; 102:15545–50] Ingenuity Pathway Analysis and Gene Ontology/GATHER. These results were then compared to GEP data from a patient subset (n=76) treated with single-agent dexamethasone and also cell lines selected for differential sensitivity to bortezomib. Results: A comparison of R and PD highlighted numerous differentially expressed genes; approximately 200 were over-expressed in R and 500 were over-expressed in PD (t-test <0.01). R-associated genes include signaling molecules like TRADD, CFLAR and TANK, while PD-associated genes included oncogenes (NRAS), apoptotic regulators (DAP3) and cancer antigens (CTAG). Pathway tools helped to integrate and extend these observations, consistently identifying NFkB activation and overexpression of adhesion pathways as R-associated. Overexpression of protein synthesis and mitochondrial pathways were PD-associated. The overexpression of pathways such as NFkB, adhesion and cytokines appear linked specifically with bortezomib and not dexamethasone sensitivity. These approaches were also applied to patient survival. In contrast to analyses of single genes, the pathway tools highlighted links between response and survival, including adhesion and inflammatory cytokine pathway expression. Conclusions: This study highlights the diversity of biological pathways expressed in MM and the utility of clinical genomics to associate pathways with activity of single-agent bortezomib.

Abstract Abstract 621 Background: Two- and three-drug regimens incorporating bortezomib (Velcade®, V), lenalidomide (Revlimid®, R), dexamethasone (D), or cyclophosphamide (C) have been shown to be effective and well tolerated in previously untreated multiple myeloma (MM). Combining all four drugs in a single regimen (VDCR) may further enhance efficacy. Published results from the phase 1 dose-escalation portion of the non-comparative, multi-center EVOLUTION study showed that the VDCR regimen was highly active and generally well tolerated (Kumar et al Leukemia 2010). Here we present updated results from the phase 2 portion of the trial, focusing on efficacy and safety of the VDCR, VDR, and VDC regimens. Methods: Previously untreated patients with measurable disease were randomized to one of four treatment groups receiving up to eight 21-d cycles of VDCR (V 1.3 mg/m2 d 1, 4, 8, 11; D 40 mg d 1, 8, 15; R 15 mg d 1–14; C 500 mg/m2 d 1, 8), VDR (VD as in VDCR but with R 25 mg d 1–14), VDC (VDC as in VDCR), or VDC-mod (as for VDC but with an additional dose of C on d 15) as induction therapy, followed by four 42-d maintenance cycles of V 1.3 mg/m2 (d 1, 8, 15, 22) (all treatment arms). Patients eligible for autologous stem cell transplant (SCT) could undergo stem cell mobilization any time after cycle 2 and SCT any time after cycle 4. The primary endpoint was the combined complete response (CR) + very good partial response (VGPR) rate; secondary endpoints included safety/tolerability, time to response, duration of response, progression-free survival, and rate of minimal residual disease (MRD) negativity. Responses were assessed according to International Myeloma Working Group (IMWG) uniform criteria using an automated computer algorithm. Adverse events (AEs) were graded using the CTCAE v3.0. Results: Patient characteristics were similar among the groups with respect to age, performance status, ISS stage, and proportion of patients with high-risk cytogenetic features. Patients received a median of 5, 6, 6, and 6 treatment cycles in the VDCR, VDR, VDC, and VDC-mod arms, respectively; 65%, 60%, 52%, and 47% of patients had dose reductions of any drug. In the VDCR, VDR, VDC, and VDC-mod arms, respectively, 52%, 62%, 58%, and 65% of patients completed treatment; 31%, 43%, 24%, and 41%, respectively, underwent SCT. In the phase 2 response-evaluable patients (n=132), all treatment regimens showed substantial efficacy, with CR+VGPR rates of 59% (VDCR), 50% (VDR), 41% (VDC), and 59% (VDC-mod) (Table) (includes pre-transplant responses only in SCT patients). Of MRD-assessed patients, 46% (21/46) of those who achieved CR (including sCR) or nCR were MRD-negative; 48% (10/21), 75% (9/12), 0% (0/7) and 33% (2/6) in the VDCR, VDR, VDC and VDC-mod arms, respectively. Median time to first response was similar across arms (range 1.6–1.8 months); median time to best response of CR+VGPR was 4.0 months (VDCR), 3.4 months (VDR), 5.1 months (VDC), and 3.1 months (VDC-mod). Median duration of response has not been reached in any arm to date. All treatment regimens were generally well tolerated. In the VDCR, VDR, VDC, and VDC-mod arms, at least one grade ≥3 AE was observed in 81%, 76%, 79%, and 88% of enrolled patients, respectively; serious AEs were experienced by 42%, 40%, 21%, and 41% of patients, and AEs resulting in study discontinuation were reported for 19%, 17%, 12%, and 6% of patients. The five most common all-grade AEs across all treatment groups were fatigue (range 47–67%), nausea (36–67%), constipation (40–62%), diarrhea NOS (42–65%), and neutropenia (19–52%). The incidence of grade ≥3 (grade ≥2) peripheral neuropathy (PN) was 13% (40%) in the VDCR arm, 14% (45%) VDR, 9% (48%) VDC, and 18% (41%) VDC-mod; there was no grade 4 PN. Rates of grade ≥3 neutropenia/thrombocytopenia were 42%/10% for VDCR, 7%/7% VDR, 36%/12% VDC, and 65%/18% VDC-mod. Conclusions: All regimens appear highly active and generally well tolerated in previously untreated MM patients. The four-drug combination did not result in a substantial increase in response rate and was associated with a modest increase in the incidence of hematologic toxicities. Continuous weekly C in the VDC regimen was associated with high response rates and rapid responses, comparable to the VDR and VDCR arms. Outcome data will be presented following longer follow-up. Disclosures: Kumar: Celgene: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; Merck: Consultancy, Research Funding; Novartis: Research Funding; Genzyme: Consultancy, Research Funding; Cephalon: Research Funding; Bayer: Research Funding. Off Label Use: Lenalidomide for treatment of newly diagnosed myeloma. Flinn:Millennium Pharmaceuticals, Inc.: Research Funding. Richardson:Celgene, Millennium, Novartis, Johnson & Johnson: Membership on an entity's Board of Directors or advisory committees. Hari:Celgene: Research Funding. Callander:Millennium Pharmaceuticals, Inc.: Research Funding. Noga:Amgen: Honoraria, Research Funding, Speakers Bureau; Millennium: Consultancy, Honoraria, Research Funding, Speakers Bureau; Ortho-Centicor: Honoraria, Speakers Bureau; Cephalon: Honoraria, Research Funding, Speakers Bureau; Celgene: Honoraria, Research Funding, Speakers Bureau. Stewart:Millennium Pharmaceuticals, Inc.: Honoraria, Research Funding; Celgene: Honoraria. Rifkin:Millennium Pharmaceuticals, Inc.: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Speakers Bureau; Amgen: Speakers Bureau; Cephalon: Speakers Bureau; Dendreon: Speakers Bureau. Wolf:Millennium Pharmaceuticals: Consultancy, Honoraria, Speakers Bureau; Genentech and Multiple Myeloma Research: Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Speakers Bureau; OrthoBiotech: Honoraria, Speakers Bureau; Celgene: Honoraria, Speakers Bureau. Estevam:Millennium Pharmaceuticals: Employment. Mulligan:Millennium Pharmaceuticals, Inc.: Employment. Shi:Millennium Pharmaceuticals: Employment. Webb:Millennium Pharmaceuticals: Employment.

Abstract Multiple myeloma is a disease of the antibody producing plasma cells in the bone marrow. It has been characterized by frequent genomic alterations including gains and losses of chromosomes. Some of these alterations result in poor clinical prognosis. Bortezomib, a first in class proteasome inhibitor, is approved for the treatment of relapsed multiple myeloma. To better understand how the genome wide changes observed in myeloma relate to prognosis and treatment response, we have used single nucleotide polymorphism (SNP) array technology to assess genotypes and DNA copy number at up to 100,000 genomic loci. DNA from 147 bone marrow tumor biopsies collected in multi-center phase II and III clinical trials of relapsed multiple myeloma patients prior to treatment with Bortezomib were hybridized on SNP arrays to assess genomic aberrations. Commonly seen genomic alterations in myeloma were observed, including deletions of chromosome 13 (37%), 1p (25%), 6q (23%), and amplifications on 1q (35%). We found that 35% of this relapsed myeloma population exhibited a hyperdiploid genome characterized by gains of chromosomes 3, 5, 7, 9, 11, 15, and 19. Other notable genomic deletions included 17p (22%), 8p (27%) and 10p (16%). A correlation of response to bortezomib therapy in patients with differential gains and losses at the chromosomal level is ongoing. Consistent with previous reports, response to bortezomib was observed in patients with poor prognostic chr 13 deletions. A more in-depth analysis of specific loci associated with clinical outcome will be discussed. In summary, this data will help further define genomic aberrations in multiple myeloma and their association with treatment outcome.

Abstract Combinations of bortezomib (V) and dexamethasone (D) with either lenalidomide (R) or cyclophosphamide (C) have shown significant efficacy. This randomized phase 2 trial evaluated VDC, VDR, and VDCR in previously untreated multiple myeloma (MM). Patients received V 1.3 mg/m2 (days 1, 4, 8, 11) and D 40 mg (days 1, 8, 15), with either C 500 mg/m2 (days 1, 8) and R 15 mg (days 1-14; VDCR), R 25 mg (days 1-14; VDR), C 500 mg/m2 (days 1, 8; VDC) or C 500 mg/m2 (days 1, 8, 15; VDC-mod) in 3-week cycles (maximum 8 cycles), followed by maintenance with V 1.3 mg/m2 (days 1, 8, 15, 22) for four 6-week cycles (all arms) ≥ very good partial response was seen in 58%, 51%, 41%, and 53% (complete response rate of 25%, 24%, 22%, and 47%) of patients (VDCR, VDR, VCD, and VCD-mod, respectively); the corresponding 1-year progression-free survival was 86%, 83%, 93%, and 100%, respectively. Common adverse events included hematologic toxicities, peripheral neuropathy, fatigue, and gastrointestinal disturbances. All regimens were highly active and well tolerated in previously untreated MM, and, based on this trial, VDR and VCD-mod are preferred for clinical practice and further comparative testing. No substantial advantage was noted with VDCR over the 3-drug combinations. This trial is registered at www.clinicaltrials.gov (NCT00507442).