ALBERT

Description: Abstract 445 Background. In newly diagnosed myeloma patients, bortezomib treatment induces high rates of complete response (CR) and very good partial response (VGPR). Recently, we published the clustering of gene expression profiles in 320 MM patients, who were included in a large prospective, randomized, phase III transplantation trial with bortezomib (PAD) versus conventional vincristine (VAD) based induction treatment (HOVON65/GMMG-HD4). We identified 12 distinct subgroups CD-1, CD-2, MF, MS, PR, HY, LB, Myeloid, including three novel defined subgroups NFκB, CTA, and PRL3 and a subgroup with no clear gene expression profile (NP). Aim. To look at the prognostic impact of these 12 clusters in the trial and group clusters together into a high risk (HR) and low risk (LR) group in the different treatment arms. Furthermore, to define a high risk signature to identify the patients at increased risk of disease progression. Methods. Gene expression profiles of myeloma cells obtained at diagnosis of 320 HOVON65/GMMG-HD4 patients were available. Response, progression free survival (PFS) and overall survival (OS) data were available for the first 628 patients, resulting in analysis of gene expression in relation to prognosis in 229 patients. The prognostic impact of the genetic subgroups separately and grouped into high and low risk were evaluated using Kaplan Meier and Cox regression analysis using exhaustive search (R). For the high risk gene signature the HOVON65 gene expression data was used as training set with PFS as outcome measure. Two independent myeloma datasets with survival data were used as an external validation, UAMS (GSE2658) and APEX (GSE9782)). The signature was generated by a Cox proportional hazard model in combination with LASSO (Least Absolute Shrinkages and Selection Operator) for simultaneous parameter estimation and variable selection using the R package glmnet. ISS stage was implemented by adjusting the individual covariant penalization factors of the LASSO. Results. The highest CR+nCR rates were found in the PRL3 and NP clusters, i.e. 78% and 86%, respectively (VAD), and 100% (PAD). The lowest CR+nCR rate was 17% in the CD1 cluster (PAD) and 0% in the CD2, MF and PR clusters (VAD). Based on the impact of clusters on PFS and OS in the VAD arm, the MS, MF, PR and CTA clusters were included into a High Risk (HR) group. This HR group showed a median PFS of 13 months and OS of 21 months vs. the Low Risk (LR) group consisting of the remainder of clusters with a median PFS of 31 months and a median OS not reached (P

Description: Introduction Multiple myeloma (MM) is characterized by a highly variable disease course, which can be traced to initiating and acquired genomic events. Whole exome analysis of matched tumor and germline DNA from 287 MM patients identified recurrently somatically mutated genes (RSMGs) (Lohr et al. - Cancer Cell 2014, Bolli et al. - Nat Commun 2014). Despite the fact that these RSMGs affect pathways that are biologically important in MM, the clinical relevance of many of these genes in the context of conventional prognostic markers remains to be elucidated. Aims The aims of this pilot study were: (1) To validate the prevalence of RSMGs in our newly diagnosed MM patient cohort; (2) To assess the correlation between RSMGs, clinical parameters and outcome; (3) To thereby identify the potential clinical usefulness of introducing RSMG mutational profiling in larger MM trial cohorts. Material and Methods CD138+ enriched MM cells and peripheral blood were obtained with informed consent from chemotherapy-naive patients, participating in 3 clinical trials: HOVON-65/GMMG-HD4, HOVON-87/NMSG-18 and Carthadex (EudraCT number 2004-000944-26, 2007-004007-34 and 2009-014922-40, respectively). Matched tumor and germline DNA were sequenced on an Ion Torrent sequencing platform (PGM, Life Technologies), using the M3 P Mutational Panel v3.0, comprising 1327 customized oligos (Life Technologies), targeted at the coding sequences of 88 MM-relevant genes, including the RSMGs. Somatic mutations were considered positive when present in 〉=10% of tumor reads and

Description: Chromosomal region 1p22 is deleted in ~20% of multiple myeloma patients, suggesting the presence of an unidentified tumor suppressor gene in this region. Using high resolution copy number arrays, we delimit a 58 kb minimal deleted region on 1p22.1 encompassing two genes: ectopic viral integration site 5 (EVI5) and ribosomal protein L5 (RPL5). Although mutations in 1p22 genes are rare in multiple myeloma, the tumor suppressor role of EVI5 and RPL5 may be supported by the fact that these genes show the highest frequency of mutations predicted to impair protein function on 1p22. Interestingly, inactivation of RPL5 was also recently described in T-cell acute lymphoblastic leukemia (T-ALL) and glioblastoma. We found that 1p22 deleted patients have significantly lower levels of EVI5 and RPL5 mRNA expression (59% and 71% residual expression respectively; p

Description: Introduction Multiple Myeloma (MM) is a heterogeneous disease with diverse gene expression patterns (GEP) across patients. This has led to the development of various signatures allowing virtual karyotyping, defining different clusters of patients, and prognostication by high risk signatures (e.g. EMC92/SKY92). Several GEP datasets exist, but may have scaling/offset differences (batch effects) in the data, e.g. due to differences in reagents used, location, etc. Batch wise normalization approaches can reduce batch effects, and have allowed successful validation of those signatures across independent datasets. Batch wise normalization requires groups of patients that have a similar distribution of clinical characteristics, and hence cannot be applied on single patients. Here we demonstrate the validity of applying GEP algorithms on single patients using the MMprofiler, enabling the application of GEP in a routine clinical setting. Materials and Methods The MMprofiler GEP assay is a standardized assay from bone marrow to data analysis and result reporting. It was used for 77 MM patients that were enrolled in the HOVON87/NMSG18 trial (73 patients) or HOVON95/EMN02 trial (4 patients). A representative reference set of 30 HOVON patients was selected from which normalization parameters were derived, to be used for normalization of a single sample against this HOVON reference dataset. The remaining 47 samples served as an independent set of samples. In addition, we have also used the publicly available GEP data from 247 patients (MRC-IX trial) as independent samples. This MRC-IX dataset has been produced using different reagents and sample work-up procedures. Therefore, it is likely that a batch effect will exist relative to the HOVON reference dataset, which may influence correctness of single sample analyses. The GEP data from the 47 and 247 independent samples were normalized using two approaches. Firstly, by batch wise mean variance normalization (i.e. across the 47 and 247 patient batches separately). And secondly, by single sample normalization using the normalization parameters from the initial 30 HOVON samples. Subsequently, several classifiers (EMC92/SKY92 etc.) were applied to the data, and their results were compared between the two normalization approaches. Results Figure 1 shows the EMC92/SKY92 scores that were obtained after batch normalization (x-axis) and single sample normalization (y-axis). For the 47 HOVON samples there is a high degree of concordance with data points close to the identity line (y=x). Only 2 out of the 47 samples would switch assignment, which is not unexpected since those 2 samples are really close to the threshold (e.g. might also switch due to technical variation). For the MRC-IX dataset, based on single sample normalization more patients would be predicted as high risk (87 (35.2%) instead of 52 (21.0%), see Figure 1), which is caused by a positive offset (i.e. intersect with the y-axis) due to the batch effect. For the Virtual t(4;14) classifier, both datasets have a very high concordance with 0 out 47 HOVON samples, and 5 out of 247 MRC-IX samples (but really close to the threshold) switching assignment (see Figure 1). Hence, even in the presence of a potential batch effect in the MRC-IX dataset, the single sample predictions are accurate. These data suggest that single sample normalization of microarray GEP is possible but requires the strict standardization of the MMprofiler assay and algorithms. Conclusions Scores for the EMC92/SKY92 signature were nearly equivalent when derived from the data following single sample normalization and batch normalization in the Skyline generated data. In the external dataset, a much higher discrepancy was found, highlighting the need to use highly standardized methods to generate Affymetrix GeneChip results. Further validation of this method is planned, and will include replicate runs systematically controlled for various conditions. Acknowledgments This research was performed within the framework of CTMM, the Center for Translational Molecular Medicine, project BioCHIP grant 03O-102. Figure 1. Scatterplots and confusion matrices of the batch (x-axis, columns) and single sample scores (y-axis, rows) of the EMC92/SKY92 signature (left), and Virtual t(4;14) classifier (right). Scores above/below the threshold correspond to high risk/standard risk (EMC92/SKY92) and positive/negative (Virtual t(4;14)). Figure 1. Scatterplots and confusion matrices of the batch (x-axis, columns) and single sample scores (y-axis, rows) of the EMC92/SKY92 signature (left), and Virtual t(4;14) classifier (right). Scores above/below the threshold correspond to high risk/standard risk (EMC92/SKY92) and positive/negative (Virtual t(4;14)). Disclosures Van Vliet: SkylineDX: Employment. Dumee:SkylineDx: Employment. de Best:SkylineDx: Employment. Sonneveld:SkylineDx: Membership on an entity's Board of Directors or advisory committees. van Beers:SkylineDX: Employment.

Description: Introduction: Bortezomib has become an important part of myeloma therapy, despite the occurrence of toxicities such as bortezomib induced peripheral neuropathy (BiPN). Since effective prophylactic treatment is lacking, onset of BiPN can only be remedied by dose reduction or stop of treatment. Here, using a genome-wide genotyping method, we investigated the potential genetic predisposition to BiPN in MM patients who received bortezomib-dexamethasone (VD) induction therapy prior to autologous stem-cell transplantation (ASCT). Methods: We performed a genome-wide association study using the Affymetrix SNP 6.0 platform. In total 469 cases from the IFM 2005-01, IFM2007-02 clinical trials or routine diagnostic were included as discovery cohort. Another 114 samples from the HOVON-65/GMMG-HD4 trial were used as validation. Patients with BiPN grade 2 or higher after initiation of bortezomib treatment were assigned as cases (n=155 in discovery, n=40 in validation) and the remaining patients that did not developed BiPN were considered controls (n=314 in discovery, n=74 in validation). Additional exclusion criteria were a minor allele frequency ≤ 5%, genotype frequency 〈 95% or Hardy Weinberg equilibrium p-value

Description: Background The introduction of proteasome inhibitors (PIs) and immune modulatory drugs (IMiDs) to multiple myeloma (MM) treatment protocols has drastically improved the overall quality and duration of response. This rationalized challenging the role of upfront high dose melphalan (HDM) followed by autologous stem cell transplantation (ASCT) versus chemotherapy alone as intensification treatment in four recent phase III trials. Yet, these showed uniformly that upfront HDM is still superior to non-transplant intensification alternatives including PIs and/or IMiDs. However, HDM's mechanism of action involves induction of interstrand crosslinks and double-strand DNA breaks that cause collateral damage in both healthy cells (toxicity) and tumor cells, which may induce strong selection pressure on the outgrowth of resistant clones at relapse. We therefore hypothesized that certain low-risk patients could save HDM as salvage treatment and that this would improve their overall survival (OS). Methods We chose to combine three robust classification tools in MM that historically have shown a consistent performance across diverse patient cohorts: (1) International Staging System (ISS), (2) deletion of chromosome 17p (del17p) by Fluorescent in Situ Hybridization (FISH) (cutoff ≥10%) and (3) SKY92 high-risk (HR) classifier, based on gene expression profiling (GEP) of MM cells. By combining these scores, we classified ultra-low risk (uLR), intermediate-risk (IR) and HR MM as follows: uLR: ISS = 1, del17p = absent, SKY92 HR = absent. IR: ISS = 2 or 3, del17p = absent, SKY92 HR = absent. HR: ISS = 1, 2 or 3, del17p and/or SKY92 HR = present. The effect of intensification treatment on progression-free survival (PFS) and OS in these risk groups was evaluated in the phase III EMN-02/HOVON-95 MM trial (EudraCT 2009-017903-28). We focused on chemotherapy-naive MM patients aged ≤65 years, who received bortezomib, cyclophosphamide and dexamethasone (3-4xVCD) as induction therapy, followed by randomization 1 (R1) between bortezomib, melphalan and prednisone (4xVMP) and 1xHDM+ASCT, R2 between bortezomib, lenalidomide and dexamethasone (2xVRD) versus no consolidation and lenalidomide maintenance for all. At baseline, bone marrow was collected in the MM biobank at Erasmus MC Cancer Institute, the Netherlands. On this, CD138+ enrichment was performed, followed by a flowcytometric purity check, FISH analysis for del17p and GEP using HG U133 Plus 2.0 arrays (Affymetrix). PFS was defined as the time from R1 to progression or death, whichever occurred first. P-values were not adjusted for multiple testing. Results In our biobank region, 435 patients were randomized between VMP and 1xHDM, of whom ISS data were available for all, del17p status for 87%, SKY92 classification for 41% and all three risk factors for 38% of patients. Of these, 33% were ISS stage I, 47% ISS stage II, 20% ISS stage III, 10% had a del17p and 20% were SKY92 HR. A total of 43 (26%), 83 (50%) and 41 (25%) patients were classified as uLR, IR and HR, respectively. Our risk classification was both significantly associated with PFS (logrank p 〈 1x10-5; 4-year PFS of 64% in uLR, 49% in IR and 24% in HR) and OS (p 〈 1x10-4; 4-year OS of 85% in uLR, 77% in IR and 47% in HR). HR patients had a superior PFS with 1xHDM over VMP (p = 0.003), confirming previous reports. OS was related to intensification arm in both uLR and HR patients, however, in opposite direction: uLR patients treated with VMP had a better OS than those treated with 1xHDM (p = 0.047, 4-year OS of 95% with VMP versus 72% with 1xHDM); whereas OS was better after 1xHDM in HR patients (p = 0.017, 4-year OS of 68% with 1xHDM versus 22% with VMP). We found a significant interaction between intensification treatment and risk group for both PFS (likelihood ratio p = 0.03) and OS (p = 0.005). This indicates that there is indeed a differential treatment effect on survival between risk groups. Conclusions Although our findings are preliminary and need further validation, we are the first to identify a low-risk, transplant-eligible MM subgroup that - in contrast to high-risk MM patients - may benefit from receiving upfront VMP versus 1xHDM+ASCT. Moreover, this illustrates for the first time how combined molecular and clinical subclassification in MM may identify patients that benefit from alternative treatment strategies, such as prolonged bortezomib and lenalidomide. Ultimately, this may result in risk-adapted treatment approaches. Disclosures Beksac: Janssen Cilag: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Deva: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Ludwig:BMS: Speakers Bureau; Cilag-Janssen: Speakers Bureau; Celgene: Speakers Bureau; Amgen: Research Funding, Speakers Bureau; Takeda: Research Funding, Speakers Bureau. Kuiper:SkylineDx: Employment. van Vliet:SkylineDx: Employment. Hajek:Takeda: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding. Dimopoulos:Janssen: Honoraria; Celgene: Honoraria; Takeda: Honoraria; Amgen: Honoraria; Bristol-Myers Squibb: Honoraria. Palumbo:Takeda Pharmaceuticals Inc.: Employment. Cavo:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Sonneveld:Karyopharm: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; BMS: Honoraria, Research Funding.

Description: Introduction Multiple Myeloma (MM) is a heterogeneous disease with highly variable survival. Gene expression profiling (GEP) classifiers, such as the EMC-92, can consistently distinguish high risk patients from standard risk patients. Other prognostic factors for MM include the international staging system (ISS) and FISH. Here we present a comparison of prognostic factors and introduce a novel stratification based on EMC-92 and ISS. Methods Scores were calculated for the GEP classifiers EMC-92, UAMS-70, UAMS-17, UAMS-80 and MRC-IX-6 for the following five studies: HOVON-65/GMMG-HD4 (n=328; GSE19784), MRC-IX (n=247; GSE15695), UAMS-TT2 (n=345; GSE2658), UAMS-TT3 (n=238; E-TABM-1138 and GSE2658) and APEX (n=264; GSE9782; for details, see Kuiper R, et al. Leukemia (2012) 26: 2406–2413). FISH data were available for the HOVON-65/GMMG-HD4 trial and the MRC-IX trial. ISS values were available for all datasets except UAMS-TT2. Univariate associations between markers and overall survival (OS) were investigated in a Cox regression analysis, using Bonferroni multiple testing correction. For pair wise analysis of markers, the significance in the increase of partial likelihood was calculated. In order to find the strongest combination (defined as the highest partial likelihood) of GEP-ISS, we compared these pair-wise on the same data. Training sets of classifiers were excluded for those analyses in which that specific classifier was tested. All survival models have been stratified for study. The calculations were done in R using the package survival. Results Prognostic value of FISH, GEP and serum markers was determined in relation to overall survival (Figure 1). GEP classifiers generally performed much better than FISH markers. Of 6 FISH markers with known adverse risk, del(17p), t(4;14), t(14;20) and del(13q) demonstrated a significant association only in one of two data sets with available FISH (HOVON-65/GMMG-HD4). GEP classifiers, on the other hand, are much more robust. Classifiers EMC-92, UAMS-70 and UAMS-80 significantly identify a high-risk population in all evaluated data sets, whereas the UAMS-17 and the MRC-IX-6 classifiers predict high-risk patients in three of four datasets. As expected, ISS staging demonstrated stable and significant hazard ratios in most studies (three out of four). Indeed, when evaluating a merged data set, both ISS and all evaluated GEP classifiers are strong prognostic factors independent of each other. Markers with additive value to each other include all combinations of GEP classifiers as well as the combination of GEP classifiers together with ISS. Tested in independent sets, the EMC-92 classifier combined with ISS is the best combination, as compared to other classifier-ISS combinations tested on the same independent data sets. The strongest risk stratification in 3 groups was achieved by splitting the EMC-92 standard risk patients into low risk, based on ISS stage I, and intermediate risk, based on ISS stage II and III. This stratification retains the original EMC-92 high-risk group, and is robust in all cohorts. The proportions of patients defined as low, intermediate and high risk for this combined EMC-92-ISS classifier are 31% / 47% / 22 % (HOVON-65/GMMG-HD4), 19% / 61% / 20 % (MRC-IX), 46% / 39% / 15 % (UAMS-TT3) and 32% / 55% / 13 % (APEX). Variability in low risk proportion is caused by the variable incidence of ISS stage I. Conclusions We conclude that GEP is the strongest predictor for survival in multiple myeloma and far more robust than FISH. Adding ISS to EMC-92 results in the strongest combination of any of the GEP classifier-ISS combinations. Stratification in low risk, intermediate and high risk may result in improved treatment and ultimately in longer survival of MM patients. This research was supported by the Center for Translational Molecular Medicine (BioCHIP project) Disclosures: van Vliet: Skyline Diagnostics: Employment. Mulligan:Millennium Pharmaceuticals: Employment. Morgan:Celgene: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees; Millenium: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees; Novartis: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees; Merck: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees; Johnson and Johnson: Consultancy, Honoraria, Membership on an entity’s Board of Directors or advisory committees. Goldschmidt:Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding. Lokhorst:Genmab A/S: Consultancy, Research Funding; Celgene: Honoraria; Johnson-Cilag: Honoraria; Mudipharma: Honoraria. van Beers:Skyline Diagnostics: Employment. Sonneveld:Janssen-Cilag: Honoraria; Celgene: Honoraria; Onyx: Honoraria; Janssen-Cilag: Research Funding; Millenium: Research Funding; Onyx: Research Funding; Celgene: Research Funding.

Description: Recently, cereblon (CRBN) expression was found to be essential for the activity of thalidomide and lenalidomide. In the present study, we investigated whether the clinical efficacy of thalidomide in multiple myeloma is associated with CRBN expression in myeloma cells. Patients with newly diagnosed multiple myeloma were included in the HOVON-65/GMMG-HD4 trial, in which postintensification treatment in 1 arm consisted of daily thalidomide (50 mg) for 2 years. Gene-expression profiling, determined at the start of the trial, was available for 96 patients who started thalidomide maintenance. In this patient set, increase of CRBN gene expression was significantly associated with longerprogression-free survival (P = .005). In contrast, no association between CRBN expression and survival was observed in the arm with bortezomib maintenance. We conclude that CRBN expression may be associated with the clinical efficacy of thalidomide. This trial has been registered at the Nederlands Trial Register (www.trialregister.nl) as NTR213; at the European Union Drug Regulating Authorities Clinical Trials (EudraCT) as 2004-000944-26; and at the International Standard Randomized Controlled Trial Number (ISRCTN) as 64455289.

Description: Abstract 302 Background: Multiple Myeloma (MM) is a plasma cell malignancy, characterized by the accumulation of malignant plasma cells in the bone marrow (BM). Prognostic factors in MM include translocations and ISS stage; still, the clinical course is difficult to predict. MicroRNAs (miRNAs) are a class of small non-coding single stranded RNAs involved in posttranscriptional gene regulation, which may be of use in defining MM prognosis and outcome. MiRNAs regulate protein levels by binding to either partially or complete complementary sites in messenger RNAs (mRNAs), leading to translational repression or transcript degradation respectively. In this manner, miRNAs play a role in critical biological processes including cellular growth and differentiation. Specific disease related miRNAs in both acute myeloid leukemia and chronic lymphocytic leukemia have been found with specific miRNA signatures associated with different cytogenetic subtypes. Until now, information available for miRNA expression in MM is limited. Methods: MiRNA expression profiling was performed in 45 newly diagnosed MM patients enrolled in the HOVON-65/GMMG-HD4 trial; a randomized, phase III trial performed to evaluate the efficacy of bortezomib prior to high-dose melphalan (HDM) for response, progression free survival (PFS) and overall survival (OS) in patients with newly diagnosed MM. As controls, four healthy BM samples were obtained from subjects undergoing BM harvest for allogeneic transplantation donorship. For all samples, BM derived CD138 selected plasma cells (PCs) with a minimum purity of 〉 80% were obtained. RNA was isolated using the miRVANA kit, with subsequent miRNA profiling by TaqMan Human MicroRNA Array v1.0. Unsupervised hierarchical clustering with the centered correlation metric with average linkage was performed using BRB-array tools 3.6.0. For survival analysis, miRNA expression was divided in quartiles: the top quartile vs the rest to identify cases with high expression and the bottom quartile vs the rest for cases with low expression. Log-rank tests for univariate association with PFS and OS were performed for each of the 365 miRNAs using the false discovery rate to correct for multiple testing. Chromosomal abnormalities t(4;14), t(11;14), t(14;16) and deletion 13q14 were determined by FISH analysis. MiRNA expression was compared to mRNA expression, available for 39 out of 45 MM patients, using a Spearman's rank correlation test. mRNA expression was determined by Affymetrix U133 Plus 2.0 arrays (Broyl et al., Blood 2010). Results: Clustering resulted in a dendrogram with 5 clear branches, consisting of 4 MM clusters and 1 normal BM cluster. The MM clusters are characterized by up- and downregulation of distinctive miRNAs: cluster A: upregulation of miRNA clusters miRNA-17∼92 and miRNA-106∼25 (n=23); cluster B: upregulation of miRNA-130a and miRNA-424 (n=8); cluster C: upregulation of miRNA-576 and miRNA-106b (n=9) and cluster D: upregulation of miRNA-372 and miRNA-200a (n=4). An additional cluster of one sample was not defined. MiRNAs predominantly expressed in normal BM were miRNA-28 and miRNA-30c. None of the miRNA clusters correlated with cytogenetic subgroups, i.e. deletion 13q14, t(4;14), t(11;14), and t(14;16) Still, a supervised approach showed significantly higher expression of miRNA-122a, miRNA-33, miRNA-489, miRNA-519e, and miRNA-555 in patients with t(11;14). Upregulation of let-7f, miRNA-194 and miRNA-296 expression were found to be associated with better OS with borderline significance (P = .06). Finally, a significant inverse correlation between miRNA-21 expression and gene expression of two of its validated targets, PDCD4 (P = 2× 10−4) and RECK (P = 8×10−4) was found. PDCD4 is a novel tumor suppressor, whose functions include inhibition of translation through eIF4A/eIF4G. RECK has been shown to be involved in angiogenesis, through inhibition of MMP2 and MMP9. Conclusion: miRNA expression in MM is deregulated compared to normal PCs and MM patients can be classified according to their miRNA expression pattern in four clusters. Furthermore, a trend was found for high expression of miRNAs let-7f, miRNA-194 and miRNA-296 with increased OS. Integration of miRNA and mRNA data shows the putative interaction between miRNA-21 and two of its validated targets; tumor suppressor gene PDCD4 and RECK, suggesting a functional relationship between miRNA expression and development of MM. Disclosures: Sonneveld: Johnson & Johnson: Membership on an entity's Board of Directors or advisory committees; Millennium Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees.

Description: Introduction A phase 2 dose escalation trial of Carfilzomib in combination with Thalidomide and Dexamethasone (KTd) for induction and consolidation in newly diagnosed, transplant-eligible patients with multiple myeloma (MM). We report the results of 4 dose levels. Methods In this multicenter, open-label, phase 2 trial, transplant-eligible patients aged between 18 and 65 years with previously untreated symptomatic MM were included. Patients were treated with 4 cycles of escalating dose of Carfilzomib + fixed-dose thalidomide and dexamethasone (KTd) for induction therapy. The dose of Carfilzomib was 20 mg/m2 i.v. on days 1, 2 followed by 27 mg/m2 on days 8, 9, 15, 16 of cycle 1 and on days 1, 2, 8, 9, 15 and 16 of cycles 2 to 4. Thalidomide dose was 200 mg orally on days 1 through 28 and Dexamethasone 40 mg orally on days 1, 8, 15 and 22. Carfilzomib was escalated to 20/36 mg/m2 in cohort 2, to 20/45 mg/m2 in cohort 3 and to 20/56 mg/m2 in cohort 4. Induction was followed by stem cell harvest after Cyclophosphamide priming (2 to 4 mg/m2) and G-CSF. Hereafter patients received high-dose Melphalan (HDM, 200mg/m2) and autologous stem cell transplantation followed by consolidation treatment with 4 cycles of KTd in the same schedule except a lower dose of Thalidomide (50mg). The primary endpoint was response after induction therapy and overall, specifically complete response (CR) and very good partial response (VGPR). Secondary endpoints were safety, progression-free survival (PFS) and overall survival (OS). Results All 111 patients with a median follow-up of 55, 42, 35 and 28 months, in cohorts 1 to 4, respectively were included in the analysis. Median age was 58 years. ISS stages I/II/III were 41%/34%/23%, respectively, R-ISS stages I/II/III/unknown were 23%/59%/9%/9%, respectively. Of 111 patients, 9 patients stopped treatment during/after induction, 8 patients after cyclophosphamide priming or HDM and 9 patients during consolidation because of toxicity (n=9), non-eligibility for further treatment (n=6), progression (n=5), refusal (n=2) or other reasons (n=4). Overall response rate for all cohorts was 95%. Response after induction was CR/sCR in 18% of patients, ≥ VGPR in 66% of patients, ≥ PR in 94% of patients. After HDM the CR/sCR rate increased to 31% and after consolidation to 64%. Responses between cohorts were in general comparable. See Table 1. Response based on risk status by ISS/FISH in either cohort and accumulated did not show a difference in CR/sCR rate after consolidation between standard-risk (67%) and high risk defined as t(4;14) and/or del17p and/or add1q and/or ISS3 (60%). OS at 30 months was comparable between standard risk and high risk, 91% versus 90%. PFS at 30 months for standard risk and high risk was 79% and 62%, logrank p=0.02 (HR=2.3, 95% CI=1.1-4.5). PFS at 30 months per cohort was 70% (95% CI, 55% to 81%), 70% (95% CI, 45% to 85%), 80% (95% CI, 56% to 92%) and 62% (95% CI, 32% to 82%) in cohorts 1,2, 3 and 4, respectively, and 71% (95% CI, 61% to 79%) in all patients. OS at 30 months per cohort was 90% (95% CI, 77% to 96%), 90% (95% CI, 66% to 97%), 95% (95% CI, 71% to 99%) and 88% (95% CI, 58% to 97%) respectively, and 91% (95% CI, 83% to 95%) in all patients. Gene expression profiling using the Affymetrix U133 Plus 2.0 GeneChips was performed on purified tumor cells for 49 patients. Using the prognostic classifier EMC92 a high-risk group of patients (16%) was identified versus standard risk (in terms of OS: logrank p=0.06 (HR=3.7, 95% CI=0.8-16.8), and in terms of PFS: logrank p=0.14 (HR=2.1, 95% CI=0.8-6.0)). Safety analysis for all 111 patients showed non-hematological grade 3 and 4 toxicity, mainly respiratory disorders (in 15%), GI disorders (13%) and skin lesions (10%). Toxicity between cohorts did not show a significant difference. Cardiac adverse events were limited and included heart failure (n=2 at 27 mg/m2), hypertension (n=2) and chest pain (n=1 at 45mg/m2). Conclusion Carfilzomib, thalidomide, dexamethasone (KTd) is an effective regimen, with increasing CR percentages following KTd consolidation. With escalated doses of Carfilzomib responses and toxicity were comparable to standard dose of 27 mg/m2. Disclosures Zweegman: Takeda: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Kersten:Celgene: Research Funding; Amgen: Honoraria. Minnema:Celgene: Consultancy; BMS: Consultancy; Amgen: Consultancy; Jansen Cilag: Consultancy. Palumbo:Janssen Cilag: Honoraria; Takeda: Employment, Honoraria. Lokhorst:Genmab: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding. Broijl:Celgene: Honoraria; Amgen: Honoraria; Janssen: Honoraria. Sonneveld:Karyopharm: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Celgene: Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding.