Fonseca, Rafaël

Abstract Background: The free light chain (FLC) assay allows a nephelometric measurement of this free kappa and lambda light chains that circulate as monomers or dimers, not bound to the immunoglobulin heavy chain. Estimation of the free light chain levels and their ratio has utility in screening, prognostication and treatment response assessment in multiple myeloma, and is particularly useful in light chain myeloma. Previous studies have suggested that absence of a functional rearrangement on at least one of the IgH allele is the basis of light chain myeloma. Translocations involving the immunoglobulin heavy chain (IgH) locus on chromosome 14 can be detected using FISH in over half of the patients with myeloma. We hypothesized that presence of an IgH translocation may be associated with compromised production of heavy chains and leads to unbalanced, excess production of clonal light chain. Methods: From February 1988 to May 1992, 653 patients with previously untreated multiple myeloma were enrolled on the phase III clinical trial E9486. Serum free light chains were run on stored sera obtained at baseline in 495 patients as part of a previous study. Among these patients, 314 patients were previously studied using FISH for presence or absence of IgH translocations, p53 abnormalities and deletion 13 and were included in the current analysis. Results: The median difference between the involved and uninvolved FLC (FLCdiff) was higher among the group of patients with any IgH translocation abnormality compared to those with none (Figure 1). This was particularly evident among the patients with a t(14;16) who had the highest FLCdiff. In a multivariate analysis, we examined if the prognostic value seen with FLCdiff was independent of the presence of IgH translocations t(4:14) and t(14;16), which are associated with a worse outcome. The presence of t(4;14) or t(14;16) was associated with FLCdiff groups using a cutoff point of 185, with 13% of those with FLCd ≤185 and 27% of those with FLCd > 185 having the translocations (P= 0.008). At the cutoff value of 185, the prognostic value of the FLC-diff on progression free and overall survival appears to be independent of these high risk IgH translocations (P-value=0.03 and 0.003, respectively for PFS and OS) We then examined the prognostic value of the FLC-diff in the group of patients without these two high risk abnormalities and were able to confirm that they remained prognostic for PFS (P=0.009) and OS (p=0.005). However, among the group of patients with either of the two high risk IgH abnormalities, FLCdiff estimate added little to assessment of their outcome (P=0.97 and 0.20, respectively for PFS and OS). Conclusion: Elevated free light chain levels are associated with presence of IgH translocations and these patients are more likely to harbor the high risk genetic abnormalities. Routine FISH testing for patients with newly diagnosed myeloma will allow us to confirm these and allow better risk stratification of the patients. Additional work needs to be done to help us better understand the molecular basis for unbalanced light chain excretion by myeloma cells and the current finding of the association with IgH translocation abnormalities should form the basis for this work. Figure 1. Distribution of FLC ratio (inv vs. uninv) and FLC difference (inv − uninv) among patients. Solid squares represent patients with t(4;14) or t(14;16) or t(11;14) or other 14q32 translocation; open circles represent patients without t(4;14), t(14;16), t(11;14) and any other 14q32 translocations. The horizontal dashed lines refer to FLC ratio original values of 100 and 1000, and vertical dashed lines refer to FLC difference original values of 10 and 100. Solid line indicates significant association (P<=0.05) between the dichotomized FLC groups and translocation status. Figure 1. Distribution of FLC ratio (inv vs. uninv) and FLC difference (inv − uninv) among patients. Solid squares represent patients with t(4;14) or t(14;16) or t(11;14) or other 14q32 translocation; open circles represent patients without t(4;14), t(14;16), t(11;14) and any other 14q32 translocations. The horizontal dashed lines refer to FLC ratio original values of 100 and 1000, and vertical dashed lines refer to FLC difference original values of 10 and 100. Solid line indicates significant association (P<=0.05) between the dichotomized FLC groups and translocation status.

Abstract Deletion 13 multiple myeloma (MM) is detected in nearly 50 % of patients diagnosed with MM and confers a shorter survival. A region of minimal deletion was identified for chromosome 13 using Agilent 500K aCGH arrays that included microRNAs 15a and 16- 1. MicroRNAs (miRs) are small RNAs that negatively regulate gene expression through degradation of mRNA transcripts or translational inhibition. In order to determine the contribution of deletion of miRs 15a and 16-1 to MM progression, miR precursors were transfected into KMS-11 and JJN3 adherent myeloma cell lines and total RNA hybridized to Affymetrix U133 Plus 2.0 gene expression arrays for the purpose of identification of mRNA target transcripts. Thirty nanomolar miR 15a and 16-1 precursors and a nonsilencing siRNA control were transfected into adherent KMS-11 and JJN3 myeloma cell lines. Cultures were harvested 16 hours after transfection to minimize the downregulation of transcripts that are not direct targets of miRs 15a and 16-1. Total RNA was extracted using the miRNeasy kit to allow retention of the miR fraction for RT-PCR confirmation of miR over-expression following transfection. Following transfection of miR precursors, expression of miRs 15a and 16-1 were increased 64 and 128-fold, respectively, compared to non-silencing control. Total RNA was hybridized to Affymetrix gene expression arrays using protocols supplied by the manufacturer. Transcripts down-regulated following miR transfection were compared to mathematical models for prediction of miR targets. Additionally, the 3′ UTRs of down-regulated transcripts were inspected for complementarity to miR 15a and 16-1 seed sequences. RT-PCR validation of identified targets was performed. Cross reference of down-regulated transcripts with the TargetSCAN and PictarVERT miR prediction algorithms resulted in a list of 9 genes that represented potential miR-15a/16-1 targets in MM. This list included: FGF2, BCL2, CCNE1, V-MYB, WEE1, E2F7, CDK6, CDC25A and CDC27. Following target identification, reporter constructs were used to confirm direct regulation of transcripts by miRs 15a and 16-1. Functional investigation of miR targets was performed using siRNA reduction of identified targets followed by MTT assay and cell cycle analysis.

Abstract In an effort to identify novel genetic abnormalities in multiple myeloma (MM) we analyzed an initial set, as part of the Multiple Myeloma Research Consortium (MMRC) Genomics Initiative, of 94 MM patient samples on high-resolution array-based comparative genomic hybridization (aCGH) and gene expression profiling (GEP) technologies. The MMRC Genomics Initiative is a three-year research program spanning a full-spectrum of genomic technologies with the goal of analyzing 250 MM patient samples by 2009. Raw data from the Initiative are released in near real time through the MMRC Multiple Myeloma Genomics Portal. This initial aCGH dataset from the Agilent 244K platform represents the highest resolution gene copy number analysis with corresponding GEP released to date in MM. It has confirmed the cytogenetic definitions of MM with the data set being split between patients with hyperdiploid and non-hyperdiploid MM. Furthermore, the 244K platform has helped to refine minimal regions of change present on 1p, 1q, 6q, 8p, 13q, 14q, and 16q. A comprehensive screen of the dataset for genes residing in regions of homozygous deletion identified 105 genes. The most commonly affected gene was CDKN2C(p18) (8.3%) while 12 other regions were recurrently targeted. Several of these regions were recently implicated in the pathogenesis of MM but the large majority represents novel observations. The aCGH and GEP data were integrated using two different programs developed by the core institutions (The Broad Institute of Harvard & MIT and Translational Genomics Research Institute). First, Genomic Identification of Significant Targets in Cancer (GISTIC), which identifies highly significant regions of change and a limited set of candidate genes within these regions, was used to identify 285 genes associated with DNA content alterations. Second, Breakpoint Expression Correlation (BEC), which identifies dysregulated genes on either side of a copy number alteration, was used to identify 194 genes with expression changes associated with a breakpoint. This latter gene list includes all known IgH translocation target genes identified to date in myeloma. Overlapping the gene lists generated by GISTIC, BEC, and the homozygous deletion screen identified a number of genes previously implicated in MM such as CDKN2C(p18), CYLD, BIRC2/BIRC3, and two novel genes, FAM46C and RNF6. Ongoing data integration with RNAi and high content sequencing projects of the current Initiative holds great promise in furthering our understanding of MM. The MMRC Genomics Initiative and resulting efforts from the myeloma research community will increase our understanding of MM at the molecular level, help to identify new targets, and ultimately lead to the development of better, more effective therapies for myeloma.

Abstract PURPOSE: Recent advances in understanding of the molecular alterations that occur at the genetic and epigenetic levels in Multiple Myeloma (MM) have led to major leaps in identifying molecular pathways that regulate progression and resistance to therapeutic agents. However, despite great scientific advances at the genomic level, studies to identify signaling pathways deregulated at the functional proteomic level in MM are limited. In this study, we used a rapid and reproducible antibody-based protein microarray technique to screen the functional differences between malignant plasma cells in samples obtained from patients with MM compared to normal plasma cells (NPC) from the bone marrow of healthy volunteers. METHODS: We determined the protein expression level of 512 polypeptides in 12 samples of newly diagnosed patients with MM using high-throughput proteomic analysis with antibody-based protein microarray. Primary CD138+ sorted MM cells were obtained from the bone marrow of patients after informed consent. MM1.S was used in this study. Using immunohistochemistry and immunoblotting were confirmed. Lentivirus was used to knockdown CRIK. Gene expression datasets from the Mayo Clinic (accession number GSE 6477) and the UAMS (accession number GSE 2658) were obtained from the Gene Expression Omnibus for analysis. The Mayo dataset was generated using Affymetrix U133A platform whereas the UAMS dataset was generated using Affymetrix U133plus 2.0 platform. RESULTS: We identified four subgroups of MM using unsupervised clustering analysis. We confirmed overexpression of some of these proteins including CRIK and CDK4 using immunohistochemistry and immunoblotting. Many of these proteins are known to be deregulated in MM, indicating that this technique can accurately identify proteins that are over or under-expressed in MM in a high-throughput fashion. In addition, we identified novel proteins that are not previously known to be differentially expressed in MM, including the small GTPase member of the Rho family, CRIK protein. We then showed using knockdown of CRIK that this novel protein specifically regulates migration and adhesion in MM. There was no effect on survival of MM cells using the CRIK knockdown. Analyzing the GEP data of the 15 NPC, 46 monoclonal gammopathy of undetermined significance (MGUS) or smoldering myeloma (SMM) and 101 MM samples from the Mayo Clinic, there was a significant increase in expression of CIT from NPC to MM. Among the 351 patients entered into TTII trials from UAMS, CIT expression was similar across the different TC class. Using a cut-off normalized expression level of 1.25 (a level above expression in NPC), MM with a high CIT expression (n=81) had a significantly shorter survival than the other patients. CONCLUSION: In this study, we show that MM cells express a high level of CRIK, and that inhibition of this protein leads to significant inhibition of adhesion and migration of MM cells. In addition, CRIK protein expression correlated with CIT gene expression, with high expression in MM samples compared to NPC. In addition, high CIT expression correlated with poor survival in patients with MM.

Abstract Events mediating transformation from pre-malignant MGUS to MM is currently not well defined. Recurrent genetic abnormalities such as t(11;14), t(4;14), hyperdiploidy and chromosome 13 deletion are already present in MGUS at relatively similar frequency to MM. The unified deregulation of D-type cyclins is also already present in MGUS. Previous GEP studies have revealed little differences between MGUS and MM. A more recent study using a large patient cohort and new generation Affymetrix genechip identifies an MGUS signature but the functional and biological significance underlying this signature is unknown. In the current study, we analyze a cohort of 22 MGUS and 101 MM from the Mayo Clinic with GEP performed on Affymetrix U133A genechip using gene set enrichment analysis, a method that analyze differentially expressed gene between 2 phenotypes of interest in the context of published or curated genesets that represent specific biological, chemical, or molecular perturbation to cells. This method increases the sensitivity of identifying low but significant changes in gene expression. We made further modification to the original method that allows assessment in individual samples rather than the average across a phenotype further increasing the specificity of the output. In this analysis, 313 genesets were significant enriched for genes over-expressed in MM compared to MGUS, representing potential activated pathways that mediate transformation. When MM samples and genesets were clustered using the enrichment score for each genesets and samples, 4 cluster of genesets emerged, one including a number of MYC genesets, one including a number of cell cycle related genesets, one including genesets related to metabolic activity and another including a number of IFN related genesets. Further dissection of these correlated genesets to identify common enriched genes (leading edge genes) led to identification of a MYC core signature, tRNA core signature, Proteosome core signature and metabolic core signature which are highly correlated. From known literature and biology, it is likely that MYC activation leads to downstream activation of protein synthesis (tRNA signature), degradation (proteasome signature), and metabolic pathway (metabolic signature). This is verified by GEP results from in vitro modulation of MYC activation in cell lines. In addition, a cell cycle core signature and IFN core signature was identified. Activation of IFN and MYC core signatures accounts for almost 90% of MM patients. The remaining patients have a metabolic signature without MYC or IFN activation. The activation of the different core signatures is significantly correlated with certain TC classes when assessed by Chi-Square test. IFN activation is significantly correlated with D1 subtypes and negatively correlated with D2 subtypes. MYC activation is negatively correlated with t(11;14). Similar patterns were observed in a validation dataset of 351 MM patients from UAMS (GSE2677) with GEP performed on the U133plus2.0 chip. These results are validated at the protein level using IHC on TMA of the Mayo cohort. The activation of the IFN and MYC pathway may represent predominant mechanism in MGUS to MM progression.

Abstract Background Multiple myeloma (MM) is presently an incurable plasma cell (PC) malignancy which involves 16 000 new patients per year in US. In spite of the abundance of cytogenetic information, with more than 600 published karyotypes in Non-hyperdiploid MM (NHMM), very little is known about the mode of karyotypic evolution or of the presence of possible cytogenetic pathways. In our investigation, we have used 647 NHMM karyotypes, to identify the most frequently occurring imbalances. Patients and Methods The Mitelman database of Chromosome aberrations in cancer (Mitelman et al., 2007) was searched, and myelomas with modal chromosome number less than 48 were selected. All karyotypes were described using ISCN nomenclature (ISCN, 1995). Only the main clones were analyzed and incomplete karyotypes were excluded. The following information was obtained from each karyotype: chromosome number, imbalances including whole chromosome gains and losses; specific translocations and breakpoints, including add chromosomes. An Excel spreadsheet in which the rows of the first column were labeled to represent each band of a 360-band human chromosome ideogram was created as previously described (Hoglund et al., 2001; Horsman et al., 2003) All karyotypes were scored individually in successive columns for net gain and loss of each chromosome band. The total number of gains and losses affecting each chromosome band for the total cohort of karyotypes was then calculated using the Excel countif function. Recurrent abnormalities were defined as those that were involved in >5% of cases. Histograms of the number of abnormalities per tumor were then plotted, and the modal value extracted as variable called time to occurrence. Statistics correlation between the presence and absence of different abnormalities was analyzed using Spearman correlation matrices. P value of <0.05 was considered as statistical significant. Results In total 647 cases of newly diagnosed NHMM were included in the analysis. The median chromosome number was 43. Few cases had chromosome numbers under 40. The median NAPT was 12 (1–49) with a normal distribution (Fig 1). The most common abnormalities were monosomies of the chromosomes 13 and 14 (32.44% and 14.88% respectively). Out of 647 cases, more than 300 NHMM cases contained translocations in their karyotypes. The most common translocation was t(11;14), which occurs in 17% of cases followed by t(1;16) in 2.7% of cases and t(4;14) and t(8;14) in 1.5% respectively. The most recurrent breakpoints in our series include: 14q32 (32%), 11q13 (20%), 1q10 (10.5%), 1q12 (8%), 8q24 (6.9%), 1p11 (6.6%) and 1q21 (5.5%). Discussion Although only about 40% of MM has informative karyotypes, karyotyping has the advantage of representing global genetic changes at the cellular level. In this comprehensive survey, loss of chromosome 13 and 14 were the most common abnormalities. The NAPT analysis suggests that most NHMM patients have at least 12 abnormalities at diagnosis. It is possible that accumulation of abnormalities above a threshold results in malignant transformation. In our series the t(11;14) translocation appears to be the most recurrent translocation and exceeds the rate reported in Hyperdiploid-MM (17% vs 3.8%). Figure 1. NAPT Distribution in NH-MM Figure 1. NAPT Distribution in NH-MM

Abstract Background: Venous thrombotic events (VTE) are a common complication of therapy with the lenalidomide plus dexamethasone regimen. The incidence of VTE with RD is approximately 20%, and can be lowered with the use of effective thromboprophylaxis, avoidance of erythropoietin, and the use of lower doses of dexamethasone. The goal of this study was to determine the impact of VTE on overall survival of patients with newly diagnosed myeloma by studying events occurring in ECOG E4A03 phase III trial of lenalidomide plus high (standard) dose dex (RD) versus lenalidomide plus low dose dex (Rd) in newly diagnosed myeloma (MM). Methods: Pts with untreated, symptomatic MM were eligible. Pts in the RD arm (Arm A) received lenalidomide 25 mg/day PO days 1–21 every 28 days plus dex 40 mg days 1–4, 9–12, and 17–20 PO every 28 days; pts in the Rd arm (Arm B) received lenalidomide at the same dose plus dex 40 mg days 1, 8, 15, and 22 PO every 28 days. The trial initially did not mandate routine thromboprophylaxis, but recommended that such treatment be considered. After the first 264 patients were enrolled the trial was amended to require mandatory thromboprophylaxis of aspirin for all patients, with a recommendation to use stronger thromboprophylaxis with either warfarin (target INR 2–3) or low molecular weight heparin among patients in the RD arm. Results: 445 pts (median age, 65 yrs) were accrued; 223 randomized to RD, 222 to Rd. Median follow-up time is 30 months. Overall VTE including deep vein thrombosis (DVT) and pulmonary embolism (PE) occurred in 18.5% of patients; 25.6% in Arm A and 11.4% in Arm B. Rates for the first 4 cycles of treatment were 20.2% in Arm A and 8.2% in Arm B, P<0.01. Rates did not change substantially before and after the prophylaxis amendment. A partial response (PR) or higher was seen in 82.1% of pts who experienced VTE compared with 74.6% of pts who did not experience VTE, P=0.19. Overall VGPR rates also were not inferior. Pts who had VTE, however, had significantly higher other grade 3–5 toxicities such as hyperglycemia (14.6% vs 7.5%, P=0.051), cardiac ischemia (4.9% vs 0.8%, P=0.002), non-neuropathic weakness (13.4% vs 6.4%, P=0.039), infection/pneumonia (17.1% vs 11.1%, P=0.138) and fatigue (18.3% vs 10.5%, P=0.060). In a Cox PH model, VTE status as a time-varying covariate was marginally significant: HR 1.54 95%CI (0.96–2.47), P=0.074, suggesting patients that develop VTE have a higher hazard of death. Conclusions: The occurrence of VTE may adversely affect the survival of patients with newly diagnosed myeloma receiving Rev-Dex. VTE was associated with a higher frequency of other serious adverse events. Prevention of VTE events is a priority. Besides lowering the dose of dexamethasone, studies investigating optimum thromboprophylaxis are needed.

Abstract Table 1. Samples to p53 deletions p 53 deletion FISH aCGH FISH and aCGH MGUS 142 - - SMM 108 - - Newly diagnosed MM - 182 - Relapsed/refractory MM 62 156 19 HMCLs - 48 - Total 312 386 19 Background: Inactivation of p53 by mutation or allelic loss is a rare event in multiple myeloma (MM) at the time of disease diagnosis and believed to be more common in the late stages of the disease. Here we defined the prevalence of p53 deletions in monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM), newly diagnosed MM, relapsed MM and human myeloma cell lines (HMCLs). Indirect mechanisms of p53 inactivation such as amplification of MDM2, or deletion of CDKN2A (p14ARF) were also investigated. Patients and Methods: A total of 631 MM samples and 48 HMCLs were analyzed for p53 abnormalities (Table 1) and compared with a cohort of plasma cell leukemia (PCL) previously published by us. Interphase fluorescence in situ hybridization (FISH) and highresolution array-based comparative genomic hybridization (aCGH) were used to detect p53 deletions. MDM2 amplification and p14ARF loss were evaluated by aCGH. Overall survival (OS) was estimated through Kaplan Meier method. Results: The prevalence of p53 deletions was 1.4%, 3.7%, 8.9%, 20% and 87.5% in MGUS, SMM, newly diagnosed MM, relapsed MM and HMCLs respectively (Figure 1). Of interest patients in first relapse showed a p53 deletion prevalence of 18% in comparison with 33% for patients in second relapse or more. We obtained p53 deletion status in 8 patients noted to have deletion at the time of relapse and in whom we had previous samples stored. In 7 of 8 cases deletions had been acquired (absent in the previous samples). The median OS for relapsed MM patients with or without p53 deletion were 4.2 months and 37.8 months respectively (p <0.001). MDM 2 amplification and p14ARF loss was detected in 0.4% and 2% of new diagnosed MM, 0.6% and 5.1% of relapsed MM and 2% and 29% of HMCLs respectively. Discussion: Emergence of p53 deletion/mutations denotes progression genetic events in MM. Importantly we show for the first time the overriding importance of p53 deletions as prognostic markers in relapsed MM. A molecular staging system that is based on the presence of p53 inactivation may be more powerful than classifying patients based on the loss of p53. Other abnormalities involving p53 pathway regulators like MDM2 gain and p14ARF loss are infrequent events in MM, even in advanced disease. Figure 1. Prevalence of p53 deletions Figure 1. Prevalence of p53 deletions

Abstract The Multiple Myeloma Research Consortium (MMRC) Genomics Initiative was instigated to harness the power of multiple genomic approaches to further the understanding of multiple myeloma. To date, 137 samples from patients with newly diagnosed and relapsed myeloma (out of an estimated final total of 250 by the end of the year) have been subjected to expression profiling and array comparative genomic hybridization. To identify regions of recurrent copy number alteration with a high degree of confidence, we have used the Genomic Identification of Significant Targets In Cancer (GISTIC) algorithm, which detects such regions and assigns a probability to each. Application of GISTIC to the MMRC collection identified 14 significant regions of amplification and 15 significant regions of deletion. The algorithm further detects peaks of copy number change that contribute to each region’s significance. Of genes that were expressed in our dataset, a total of 64 across the genome were found to lie within the boundaries of significant amplification peaks and 30 were found to lie within significant deletion peaks. Given the likelihood that this gene collection is highly enriched for genes important in the pathogenesis of myeloma and potential therapeutic targets, it has been prioritized for further validation with the amplified genes submitted for arrayed RNAi and the deleted genes expedited for re-sequencing. In order to define a poor prognosis group, we developed and applied a model based on the gene expression dataset of Shaughnessy and colleagues. To identify pathways activated in poor prognosis disease, we applied Gene Set Enrichment Analysis with the Molecular Signatures Database. This demonstrated enrichment of multiple canonical pathways within the poor prognosis group, including those associated with proliferation, cell cycle progression and DNA repair. A search for recurrent copy number events associated with poor prognosis disease revealed that by far the most significant event, and the only one surviving correction for multiple hypothesis testing, was mono- or bi-allelic deletion of CDKN2C (p18). Interestingly, the poor prognosis group also demonstrated higher expression of p18 in those samples without bi-allelic deletion and of CDKN2A (p16). Deletion and increased expression of p18 in high proliferation index myeloma samples has previously been described. However, our analyses demonstrate the pre-eminence of p18 loss for defining poor prognosis disease compared to other recurrent copy number changes. Furthermore, it expands on work in other tumors demonstrating feedback mechanisms between cyclin dependent kinase inhibitor pathways, suggesting a more complex model of interplay between the pathways than previously described. In conclusion, the MMRC reference collection is proving an important tool in the fight to understand key genetic events in multiple myeloma and would ultimately be anticipated to contribute towards development of improved therapy for the disease.

Abstract Melphalan (M), Prednisone (P) and Lenalidomide (R) are active in the treatment of multiple myeloma (MM). We evaluated the toxicity and efficacy of MPR in a phase I/II trial. Results of phase I part were previously reported (Blood2006, 108(11) abst 3558). M 5 mg/m2 on days (d) 1–4, 60 mg/m2 d 1–4 and R 10 mg d 1–21 of a 28 day cycle was determined as MTD for the phase II study. Methods: Patients with previously untreated MM who were not candidates for stem cell transplantation, age ≥18 yr, ECOG PS 0–3, evaluable or measurable disease, ANC >1500, platelets > 100,000 and creatinine ≤ 3.0 mg/dL were eligible. All patients received aspirin 325 mg/d as DVT prophylaxis. Routine antibacterial prophylaxis was not used. G/GM-CSF was used at the discretion of the treating physician for neutropenia. Response rate (RR), confirmed on two consecutive determinations was the primary end-point. The study had 90% power to detect a RR of at least 45%. Results: Twenty-six patients were accrued between July ’06 and Feb ’08. The median age was 74 y (64 – 87y), 17 (65%) were men. Six, 13, 6, 1 (23%, 50%, 23%, and 4%) patients had ECOG PS 0, 1, 2, and 3, respectively. Three (12%), 11 (42%) and 10 (38%) patients were ISS stage 1, 2 and 3. A total of 106 cycles were administered; median 4.5 (1–13). Seventy-seven % patients experienced at least 1 grade 3 or 4 toxicity. Neutropenia (42%/8%) and thrombocytopenia (20%/8%) were the most common grade 3/4 adverse events followed by anemia (16%/0%), fatigue (8%/4%), rash (11%/0%), and hyperglycemia (4%/0%). There were 20 treatment delays in 9 patients, and 18 dose reductions (9 in R, 11 in M and 1 P) occurred in 7 patients, most commonly because of neutropenia or thrombocytopenia. Eleven patients (42%) required G-CSF treatment. Eleven patients are currently receiving treatment; 15 have gone off study (4 completed per protocol, 3 refused, 3 adverse effects, 3 unrelated illnesses, 2 other). Median follow up of survivors is 8 m (0 – 22m). Eighteen (69 %; 95%CI 48–86) patients had a PR or better according to International Myeloma Working Group Uniform Response criteria; 3 (12 %) CR, 5 (19 %) VGPR and 10 (38%) PR. Median number of cycles before achieving a response was 2 (1 – 11). Median Progression Free and Overall Survival is 16.7m (95% CI 11.2–23.0), and 23 m (95% CI: 22 – 23), respectively. Conclusions: MPR combination is feasible with a manageable toxicity profile and has significant activity in the treatment of patients with MM who are not candidates for stem cell transplant, with an overall response rate of 69%, and a CR plus VGPR rate of 31%. Grade 3 or 4 neutropenia and thrombocytopenia was the most common toxicity, seen in up to 50% of patients in this cohort of predominantly elderly patients. Dose reduction in R and/or M was required in 27% patients. No patient developed neuropathy or thromboembolic complications.