Bergsagel, P. Leif

Abstract Over 40% of cases of multiple myeloma (MM) are associated with translocations of the immunoglobulin heavy (IgH) chain gene gene. The t(4;14) translocation, present in ca. 20% of myeloma cases, results in the overexpression of two potential oncogenes, MMSET and FGFR3, via juxtaposition of their endogenous promoters to regulatory elements of the IgH locus. The presence of t(4;14), and MMSET overexpression, is an adverse prognostic factor in MM irrespective of FGFR3 expression. MMSET contains several conserved motifs found in proteins involved in chromatin function (PWWP, HMG, PHD domains) and in the epigenetic control of transcription (SET domain). Accordingly, we found that the two main isoforms of the MMSET protein exhibit exclusive nuclear localization in both transfected fibroblasts and myeloma cells carrying t(4;14). Towards our goal of defining the ability of MMSET to affect gene regulation and contribute to the disease pathogenesis, we found that the SET domain of MMSET possesses in vitro methyltransferase activity specific for core histones H3 and H4. Using a computational approach and theoretical extrapolation from the solved NMR structure of vSET, we identified residues in the active site of MMSET essential for catalysis, whose mutation drastically reduces enzymatic activity. Reporter assays using Gal4 fusion constructs showed that both the amino terminus of MMSET, containing the PWWP and HMG domains, as well as the SET-containing carboxy terminus act as transcriptional repressors. MMSET interacts physically and functionally with a number of known co-repressor molecules, such as HDAC1, HDAC2, Sin3a, and SIRT1, but not HDAC4 or HDAC6. As such, MMSET co-expression enhances HDAC1 and HDAC2-mediated repression in transcriptional reporter assays, and MMSET repression is partially relieved by the addition of an HDAC inhibitor. A yeast two hybrid screen identified a number of other functional partners of MMSET, including ZNF331/RITA (Rearranged in Thyroid Adenoma), a KRAB domain/zinc finger protein previously implicated in malignancy. MMSET and ZNF331 co-localize in the nuclei of transfected fibroblasts, co-immunoprecipitate, and display cooperative repression in reporter assays. Collectively, these data support the idea that MMSET is a biologically active, bifunctional transcriptional mediator acting as a HMT enzyme in chromatin remodeling and as a complex adaptor in the recruitment of repressor species. Presently we are modeling the biological effects of MMSET through a conditional overexpression system in a B cell line. While low levels of MMSET are ubiquitiously expressed, induction of high levels of MMSET expression in the B cell line is associated with growth suppression and G1 arrest. While paradoxical for a presumed oncoprotein, such actions have been observed for other disease-associated proteins such as Runx1/MTG8. In contrast, a myeloma cell line harboring t(4;14) proliferates in the presence of high level MMSET expression. RNAi-mediated knockdown of MMSET in these cells induces apoptotic cell death. This suggests that MMSET may be critical for growth and survival of myeloma cells. Profiling of gene expression changes in these systems should link the transcriptional and biological activities of MMSET.

Abstract Background: Thalidomide is an active anti-myeloma agent with single agent activity in all stages of the disease. When combined with dexamethasone disease response is seen in nearly two-thirds of patients with newly diagnosed MM. Though initially evaluated for treatment because of its anti-angiogenic properties and the finding of increased bone marrow angiogenesis in MM, it has become clear that thalidomide exerts its anti-myeloma activity through several different mechanisms. While the mechanism of action of dexamethasone has been studied to a great extent in the setting of myeloma, the molecular basis for the activity of this combination is largely unknown. Methods: Myeloma cells from patients with newly diagnosed MM who were enrolled in a phase III trial (ECOG E1A00) comparing thalidomide and dexamethasone combination (T+D) with dexamethasone alone (D) were studied by gene expression profiling. Samples from diagnosis were compared to those at four months following therapy to study the effect of treatment. Four patients who achieved a partial response with T+D were studied and in addition compared to four patients achieving a partial response with D alone. Plasma cells from bone marrow aspirates were separated by magnetic bead selection of CD138 positive cells and studied using Affymetrix HG-U133A chips using standard methodology. The arrays were analyzed using Genespring 7.2 software following GCRMA normalization and genes with differential expression between the two datasets were examined. Differentially expressed genes were further analyzed using Ingenuity Pathways Analysis program Results: 549 genes were two fold or more differentially expressed between the pretreatment and post treatment samples in the T+D group, of which 275 genes were mapped to different networks using the Ingenuity program. Examination of the canonical pathways demonstrated several pathways that were altered as a result of the therapy. The most significant pathways included those related to immune function including antigen presentation (multiple HLA class II antigens), and PRG2 (NK cell activator). Several mediators of B cell signaling including CD45, PKC (α ,β), SHP2, and Erk1/2 were up regulated following treatment. Increase in the expression of several adhesion molecules including β integrins, fibronectin, VCAM1, L selectin, and LFA3 were noted. Up regulation of IL-6 signaling pathway including gp130, JAK2 and IL8 were noted following therapy. Striking increase in the gene expression for CXCR4 and its ligand SDF-1α was noted as well. 129 genes were differentially regulated by dexamethasone alone, of which 67 were mapped to different networks. The pathways influenced by the dexamethasone alone differed significantly from that of the combination. Conclusion: This exploratory analysis offers an interesting insight into the potential mechanisms of action of the T+D combination in MM. The up regulation of immune response related genes are in accordance with some of the mechanisms described for thalidomide. Also, the differences observed between the patients receiving T+D vs. D allows for assessment of the effects of thalidomide. These findings need confirmation in a larger study.

Abstract Background: We reported a high response rate with the combination of lenalidomide plus dexamethasone (Rev/Dex) as initial therapy for newly diagnosed multiple myeloma (Blood2005;106:4050–3). We now present new data on time to progression (TTP), progression free survival (PFS), and overall survival (OS) from this phase II trial, and also include updated response data. Patients and Methods: 34 patients (23 male and 11 female) were enrolled. Lenalidomide was given orally 25 mg daily on days 1–21 of a 28-day cycle. Dexamethasone was given orally at a dose of 40 mg daily on days 1–4, 9–12, 17–20 of each cycle. Patients were allowed to go off treatment after 4 cycles of therapy to pursue autologous stem cell transplantation (SCT), but treatment beyond four cycles was permitted at the physician’s discretion. For patients continuing therapy beyond 4 months, the dose of dexamethasone was reduced to 40 mg on days 1–4 of each cycle. Response was assessed by modified EBMT/International Myeloma Working Group Uniform Response criteria. All patients received aspirin (81 mg or 325 mg daily) as prophylaxis against DVT. Results: The median age was 64 years (range, 32–78). All patients were evaluable for response and toxicity. Median follow up is 21 months. Thirteen patients proceeded to SCT following initial therapy with Rev/Dex and were censored at that time point for purposes of calculation of response, TTP and PFS. Patients who discontinued therapy to proceed to SCT received a median of 4 cycles of therapy (range, 4–13), while those staying on Rev/Dex (n=21) received a median of 19 cycles of therapy (range, 2–30). Thirty-one of 34 patients (91%) achieved an objective response to therapy; including 6 patients (18%) achieving a complete response (CR) and 13 patients (38%) achieving very good partial response for a CR+VGPR rate of 56%. The CR+VGPR rate among the 21 patients staying on Rev/Dex as primary therapy without SCT was 67% (CR 24%, VGPR 43%). Median TTP, PFS, and OS have not been reached (Figure). By Kaplan-Meier method, the estimated 2 year progression rate was 18%. The 2-year PFS rate and OS rate were 74% and 91%, respectively. Fifty-five percent of patients experienced grade 3 or higher non-hematologic toxicity at any point during therapy, most commonly fatigue (21%), neutropenia (21%), anxiety (6%), pneumonitis (6%), muscle weakness (6%), and rash (6%). Two patients died on study: one attributed to infection unrelated to therapy, the patient had stopped all therapy for over a month before the fatal infection occurred; the other death was due to infection felt possibly related to therapy. One patient developed a pulmonary embolism (grade 4 toxicity), but recovered with therapy; no other patient developed deep vein thrombosis or pulmonary embolism. Conclusion: Rev/Dex is highly active and well tolerated in the treatment of newly diagnosed multiple myeloma with a high CR+VGPR rate of 56% for the trial, and 67% among the subset of patients receiving this regimen as primary therapy. Responses are durable with a low progression rate at 2 years.

Abstract Chromosomal translocations involving the immunoglobulin heavy-chain (IgH) locus at 14q32 are genetic hallmarks of human multiple myeloma (MM) and are likely primary oncogenic events in myeloma transformation. Common recurrent translocation partner genes of IgH account for 40–50% of cases and these rearrangements result in the transcriptional dysregulation of various proto-oncogenes such as CCND1, CCND3, FGFR3/MMSET, c-MAF, MAFB, MUM 1(IRF-4), c-MYC and it is becoming clear that these dysregulated genes play important roles in the pathogenesis and the treatment outcome of MM. Although the frequency of other partner genes of IgH translocations may be rare (<1%), it is possible that many genes not currently identified as translocation partners are functionally converged into several signal pathways and play important roles as well. Through global gene expression profiling (GEP) studies of a large population of newly diagnosed MM, we have recently reported that dysregulation of any one of the three D-type cyclins is seen in almost all newly diagnosed MM and most patients with high levels of CCND2 expression was highly correlated with expression of c-MAF, MAFB or FGFR3/MMSET. In order to identify novel translocation partner genes in MM we combined GEP and fluorescence in situ hybridization (FISH) analysis in a large population of newly diagnsoed myeloma. We investigated whether IGH-CCND2 and IGH-MAFA translocations are seen in patients exhibiting high levels of CCND2 expression but not expressing c-MAF, MAFB, or FGFR3/MMSET. We also looked for a breakpoint and targeted gene at a non-random breakpoint at the 11q23 locus in 20 MM patients with 11q23 abnormalities detected by conventional G-banding. Here we report evidence of an IGH-CCND2 fusion in a newly diagnosed case of MM and an IGH-MAFA fusion in a newly diagnosed MM and an MGUS and also mapped the 11q23 breakpoint to within 100 kb in a newly diagnosed MM and a human myeloma cell line, NCU-MM-1 and identified an IGL Pou2AF1 fusion in the NCU-MM-1 line. Importantly these translcoatios do not involve the MLL or ATM genes. These three genes were all highly expressed as detected by GEP or quantitive RT-PCR in the samples in which these genes were translocated to IG loci. Although the frequency of these 3 translocations are very rare, they further the concept of a critical role of CCND2 expression in myelomegenesis and identifed the transcriptional coactivator Pou2AF1, which specifically associates with either OCT1 or OCT2, in the B-cell response to antigens and required for the formation of germinal centers, as a potential new translocation target in myeloma. Finally, our strategy of combining GEP signatures and cytogenetic analyses appears to be a practical means of identifying new IG- mediated translocations in myeloma.

Abstract Cancer/testis antigens (CTAs), comprising the MAGE, CTAG, SSX and GAGE families, are expressed in different tumor types and restricted in normal tissues to germ cells of the testis. Many CTAs contain epitopes recognized by cytotoxic T cells and are potential targets for active immunotherapy. In MM, different CTAs have been shown to be expressed in cell lines and patient cells, and are associated with more proliferative and advance stage tumors. However, correlation with survival has never been reported. We undertook a global survey of CTA expression using gene expression profiling of CD138-enriched plasma-cell (PC) RNA in 15 normal, 101 MM, 22 MGUS and 24 SMM samples using the Affymetrix U133A chip (Affymetrix, Santa Clara, CA). 22 CTA genes were differentially expressed across the samples and unsupervised clustering using these genes clearly identifies a group of patients with high CTA expression. Most CTAs have significantly higher expression in MM as compared to SMM and especially MGUS, which has similar expression as normal PCs. As these antigens are not expressed in normal PCs, an upper limit of normal expression (ULN) was derived based on mean+3SD of expression in normal PCs for each CTA. A strict cut-off of normalized expression value greater than 2xULN was used to assign positive expression for each CTA genes. Using this method, the most commonly expressed CTAs are MAGE-A3 (73% of MM), MAGE-C1 (45%), MAGE-A6 (35%), SSX-1 (21%), GAGE-5 (18%) and NY-ESO-1 (14%). We then assigned patients with expression of more than 2 of these genes as having a CTA signature and correlated with clinical parameters. 45 patents (41 MM and 4 SMM) have the CTA signature. These patients have a significantly higher PCLI (median 0.2 v 0.7, p=0.0003). In fact, there is a significant correlation between the number of positive CTAs and PCLI (r=0.59, p<0.0001). Interestingly, there is no association between patients with a CTA signature and any of the recurrent genetic abnormalities (IgH translocations, chromosome 13 deletion and TC classes), yet MM patients with this signature have a significantly shorter survival (median 459 days vs not reached, log-rank p=0.017). A prognostic hierarchy exists amongst these CTAs. Only MAGE-A6, MAGE-A12, SSX-1 and SSX-5 expression has significant prognostic implications with the SSX family conferring the worse prognosis. Using a restricted panel of 3 CTAs (SSX-1, -5 and MAGE-A12), about 28% of MM patients expressing one or more of these genes can be identified with poor survival (median less than 1 year) that is independent of genetic subtypes. The genes differentially expressed between patients with and without the CTA signature are mainly involved in cell cycle regulation. Since the expression of this large set of CTAs appears to be under epigenetic control, we speculate that the same epigenetic processes that lead to dysregulation of the cell cycle also cause ectopic expression of CTA genes. As CTA expression is rare in MGUS, epigenetic processes may therefore also mediate transformation from MGUS to MM. These poor prognosis patients represent good candidates for novel therapies targeting the epigenetic machinery.

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 Hyperdiploid myeloma (H-MM) affects about 50% of MM. These patients exhibit significant karyotypic and clinical heterogeneity. In this study, we addressed possible biological heterogeneity within H-MM by gene expression profiling of CD138-enriched plasma-cell RNA from 53 H-MM patients using the Affymetrix U133A chip (Affymetrix, Santa Clara, CA). Analysis was performed using GeneSpring 7 (Agilent Technologies, Palo Alto, CA). Unsupervised hierarchical clustering performed using the top 100 most variable genes (generated by ANOVA) identifies 4 putative patient clusters. These clusters were reproduced using principal component analysis. Cluster 1 was characterized by high expression of a large group of cancer testis antigens and mitosis/proliferation related genes (TOP2A, NEK, ASPM, CENPA). Cluster 2 was characterized by high expression of genes involved in IL-6 and HGF signaling (MET, IL6R, SOCS3, SPINT1). Cluster 3 was characterized by high expression of genes involved in TNF/NF-KB signaling and anti-apoptosis (NFKB2, CFLAR, BIRC3, TNFAIP3, RRAS2). Cluster 4 was characterized by high expression of genes involve in angiogenesis (protocadherin, EDNRB). Unsupervised hierarchical clustering using the same set of genes in an independent cohort of H-MM patients (n=60) yielded the same 4 patient clusters over-expressing similar genes. The gene expression profile of these clusters were not driven by differences in trisomies (only about 25% of genes that define the clusters were located on the commonly trisomic chromosomes). Association between the 4 clusters and clinical parameters such as survival, chromosome 13 deletion (Δ13), presence of IgH translocations, PCLI, RAS mutation and p16 methylation was analyzed. Cluster 1 tumors were more proliferative (median PCLI 3.8, p<0.05). Cluster 2 was associated with presence of Δ13 (56%, p<0.05) although none of the genes that defined this cluster falls on chromosome 13. p16 methylation was also less common in cluster 2 (13%, p=0.06). Cluster 3 was associated with low PCLI (median 0.2, P<0.05). Most of the patients with p16 methylation also belonged to this cluster (67%, p=0.06). There was a complete absence of Δ13 in Cluster 4. Patients in the 4 clusters also had significantly different survival (log-rank p=0.002.). The difference in survival was independent of Δ13 since Δ13 has no impact on survival in this patient cohort. The molecular profiles of these subclasses may explain some of these associations. CTA expression is associated with proliferative tumors with poor prognosis. IL-6 is an important survival factor and HGF signaling has been associated with poor survival in MM which may explain the relative poor prognosis of this group. Cluster 3 may have better survival as it is associated with an anti-apoptotic signature rather than proliferative signature. In summary, gene expression profiling identified 4 sub-classes within H-MM (validated on an independent dataset and using different clustering techniques) with distinct clinical and biological associations. The gene signature associated with each sub-classes provide a molecular basis for the different clinical behaviour and further insights into deregulated pathways in H-MM.

Abstract GX15-070 is a small-molecule antagonist of the BH3-binding groove of the Bcl-2 family of proteins and can thus induce apoptosis by inhibiting the interaction between pro- and anti-apoptotic proteins. In addition, GX15-070 activates a cellular pathway, which leads to the activation of the pro-apoptotic BH3-only protein Bim, a natural cellular inhibitor of anti-apoptotic members. Together, the direct inhibition of anti-apoptotic Bcl-2 family proteins and the activation of pro-apoptotic Bim by GX15-070 can sensitize tumor cells to apoptosis. Bcl-2 family members, particularly Bcl-XL and Mcl-1 have been implicated in the survival of myeloma cells and may be responsible, in part, for drug resistance. Consequently, GX15-070 was tested against the MMRC 12 validation cell line panel, a panel of 12 standardized and annotated human myeloma cell lines (HMCLs). Western blots preformed on 8 HMCLs confirmed expression of Bcl-2, Mcl-1 and/or Bcl-XL. Basal levels of Bcl-2 and MCl-1 were relatively similar across all HMCLs studied whereas levels of Bcl-XL were variable GX15-070 inhibited the viability of all 12 myeloma cell lines as determined by MTT assay. The mean IC50 was 215 nM; range (6.4-678nM) and sensitivity did not correlate with level of Bcl-2, MCL-1 or Bcl-XL expression. On the other hand cytotoxicity to normal human blood lymphocytes, stroma and bone marrow CFUs was not observed at concentrations which were effectively cytotoxic to HMCL. Time course experiments demonstrate that apoptosis and cleavage of caspase 3 began by 12 hours and continued to increase over a 96 hour period. Co-culture with human bone marrow stroma cells (BMSCs) failed to protect HMCL from GX15-070 induced cytotoxicity. Similarly, potent MM growth factors, Interleukin 6 (IL-6) and Insulin like growth factor (IGF)-1 did not confer resistance to GX15-070. Of interest, overexpression of Bcl-2 has been shown to attenuate PS-341-induced cell death (Mitisades); however, the combination of PS-341 and GX15-070 did not reveal synergistic interaction in preliminary results. Studies looking at other drug combinations are planned. Finally, GX15-070 produced cytotoxic responses in the first primary myeloma sample tested with further testing ongoing. Mouse xenograft studies are also ongoing and will be presented. These studies indicate extensive pre-clinical activity of GX15-070 in multiple myeloma and set the stage for planned Phase II clinical trials.

Abstract Hyperdiploid (>48 chromosomes) multiple myeloma (H-MM) and high hyperdiploid (>50 chromosomes) acute lymphoblastic leukemia (H-ALL) are characterized by aneuploidy and multiple recurrent trisomies (chromosome 3,5,7,9,11,15,19 for H-MM and chromosomes X,4,6,10,14,17,18,21 for H-ALL). Little is known about the oncogenic events, consequences of the trisomies and reasons for the different recurrent trisomies. In an attempt to answer these questions, we undertook a combined gene expression and network/pathway analysis approach. Gene expression data was generated using the Affymetrix U133A chip (Affymetrix, Santa Clara, Ca) for 53 H-MM and 37 non-hyperdiploid MM (NH-MM) cases using CD138-enriched plasma-cell RNA. Gene expression data using the same chip for ALL was obtained from previous published data (Ross ME et al Blood2004; 104: 3679–3687). Analysis was performed using Genespring 7 (Agilent Technologies, Palo Alto, CA). By comparing the median expression of all genes on each chromosome between H-MM/H-ALL and their non-hyperdiploid counterparts (NH-MM and NH-ALL) for the 23 chromosomes (excluding Y), one can clearly identify the commonly trisomic chromosomes in H-ALL and H-MM. However, the relationship of gene expression was highly variable for H-MM and NH-MM as compared to H-ALL and NH-ALL which tended to have expression ratios close to 1 for the non-trisomic chromosomes. Sixty-nine percent of the differentially expressed genes generated by ANOVA analysis (p<0.001) in H-ALL were on the commonly trisomic chromosomes and were upregulated whereas the corresponding figure in H-MM is 40%. These similarities and differences probably reflect both an overall gene dosage effect and the different complexities of the karyotypes of H-MM and H-ALL compared to NH-MM and NH-ALL respectively (MM karyotypes are more complex, hence difference between H and NH-MM is greater and less confined to the trisomic chromosomes). We next performed network analysis using a curated web-based software (MetaCore, GeneGo Inc, St Joseph, MI) using the 2 sets of differentially expressed genes. Majority of genes differentially expressed in H-MM are involved in mRNA translation/protein synthesis whereas the genes differentially expressed in H-ALL were mainly involved in signal transduction. Therefore the transcriptional program that characterize the difference between H and NH-MM/ALL seem to recapitulate normal cellular function: protein synthesis in the mature secretory plasma cells and signal transduction in response to cytokines in a differentiating early-B cell. However, due to the concurrent deregulation of many genes on these trisomic chromosomes, these and other cellular programs are deregulated resulting in malignant transformation. We also intersected the 2 lists of differentially expressed genes to find genes that are up- or downregulated in both H-MM and H-ALL relative to the NH tumors. Thirteen genes including interferon response genes (TNFSF10, MX1, ZNF185) and transcription factors like RUNX1 were upregulated, whereas 13 genes including a cancer testis antigen gene (MAGED4) were downregulated in both H-MM and H-ALL. These genes may point to common oncogenic mechanisms.

Abstract Introduction: The Multiple Myeloma Research Consortium (MMRC) has established a tissue bank for the deposition of bone marrow (BM) samples from patients with multiple myeloma (MM) to be mailed and processed under good laboratory practices (GLP). To date over 300 samples have been collected. The ability for multiple sites to utilize the same GLP protocols for isolation and storage of BM for batch testing can be cost prohibitive and can introduce derived variability. Currently, limited information is available on shipped BM aspirates in regards to cell viability, cell yield, cell purity, and subsequent RNA yield and quality. Materials and methods: To test these determinants we performed a pilot study on behalf of the MMRC where samples were drawn at Mayo Clinic Rochester (MCR) pooled and split into 2 equal aliquots. One-half of each sample was processed following the provided GLP compliant standard operating procedures (SOP’s), immediately after sample procurement, at MCR. The CD138+ cells were stored in TRIZOLTM and the RNA was isolated and analyzed in a single batch. The other half of the aspirate was sent overnight to Mayo Clinic Scottsdale (MCS) where they were processed using an identical protocol. At both locations samples were tested for the following quality determinants; cell yield, RNA yield and integrity, and viability using a 3 color flow cytometric method (CD45, CD38 and 7ADD). Cell counts were performed on the CD138+ fraction to determine plasma cell recovery and a slide based immunofluorescent assay used to determine purity. RNA recovery and integrity were assessed using the Agilent BioanalyzerTM. Lastly, gene expression profiles was compared to determine the “signature” emanating from the shipment of samples. Results: In aggregate, all quality determinants showed similar values when the two sets of samples were compared. Cell viability was similar in both sets of samples as was our ability to collect a highly enriched plasma cell population. The cell yield was very similar (r2=0.52) but slightly lower in the shipped samples (median 71% of locally processed, range 40–140%), probably due to some shipment-associated apoptosis with subsequent loss of cell surface CD138 antigen. The purity of the shipped samples was very similar to that of locally processed (median 94%). Subjective qualitative analysis of the RNA was similar between both groups (shipped yield being 80% of local) with no evidence of degradation in the shipped samples. Details regarding the shipment signatures using gene expression profiling will be presented at the meeting. Conclusion: Here we show that the shipment of samples is feasible with no appreciable loss in cell yield or quality of derived products. % APOPTOTIC+ DEAD PC YIELD PURITY (%) RNA Recovered MCR MCS MCR MCS MCR MCS MCR MCS 8.9 ND 15.2 6.8 93 90 11.4 12.8 6.2 ND 2 2.8 95 99 8 17.2 17.4 8 1.4 1.4 75 88 2.2 0 2.1 18.7 2.8 1.6 96 68 7.2 2 18.1 9.5 1.2 .8 100 94 3.4 .4 5 5.0 8.5 7.2 98 91 12.6 17.8 3.7 5.1 .4 .4 88 87 ND ND 19.5 10.3 1.6 1.2 88 79 .4 3.6 29.1 ND 9.2 1.2 100 56 30.2 1.4 5.6 ND 9.2 7.6 92 88 5.2 16.8 20.3 62.8 2.4 .8 84 61 4.6 0.2 4.6 3.7 10.4 2.4 100 94 29.4 8.4 26.5 13.5 5.8 2.8 ND 97 16.8 5.8 4.5 6.2 2.2 2 100 99 10 4.4 17.5 ND 19.2 12 ND ND 16.4 14