Blood, Emily

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 Although multiple myeloma is clinically defined as the accumulation of clonal, malignant plasma cells in the bone marrow, the disease shows significant heterogeneity with regard to progression and therapeutic response. It is likely that germline genetic polymorphisms contribute significantly to an individual’s disease course and response. Bank On A CureTM (BOAC) is a collaborative project initiated with the International Myeloma Foundation (IMF) to develop a DNA bank of myeloma patient samples, through international cooperation. As the bank continues to expand, it provides a resource for the development of cooperative partnerships to assess specific clinical trials as well as conglomerate data. In this study, we have combined data from 3 phase III trials: ECOG trial E9486 (VBMCP + cyclophosphamide or interferon); SWOG trial 9321 (VAD induction followed by randomization to PBSC-supported high dose therapy versus standard dose therapy of VBMCP); and the MRC7 trial (comparing conventional chemotherapy [ABCM] with intravenous chemotherapy and high dose melphalan [CVAMP + HDM +PBSCT]). Five single nucleotide polymorphisms were examined based on associations demonstrated in previous individual trials: GSTP1, IL-6, LT-a, TNF-a, and ERCC2 (DNA repair). Patient samples included 412 from E9486, 569 from S9321, and 232 from MRC7 (total=1213). As a combined data set, genotype associations with overall survival did not reach statistical significance, suggesting SNP associations with survival may be therapy specific. One single association that did reach significance in the conglomerate data was TNF-a genotype association with age of disease onset (the high producer variant AA genotype showing a 5 year later age of onset than the AG/GG genotypes, or the overall mean of the trial, age 61 vs 56; p=.03). This is interesting in light of reports suggesting high production of TNF-a is found in myeloma patients and may actually promote myeloma. Yet, high production may also delay disease onset due to its role in other immunologic processes. Additional parameters including genotype associations with bone disease, CR rates, toxicities, and stage are being evaluated and will be presented. Preliminary results show that when SNPs from S9321 were included in a linear regression model, genotype risk groups could be identified; showing 73% correct association with ISS stage 1, and 75% correct association with ISS stage 3. This model, however, was not validated in the E9486 trial, with less than 50% genotype predictive ISS stage association. A number of highly significant ethnic genotype associations were noted, and are presented in a separate abstract. Given the likely complex interactions of multiple genetic variations, collective genotype comparisons may be required from larger pooled data sets BOAC is assembling to increase significant associations.

Abstract Background: Multiple myeloma (MM) is an incurable disease with a anticipated overall survival (OS) ranging from months to decades. Modest improvements in OS have been made with high-dose chemotherapy with peripheral blood stem cell transplant (PBSCT), but to date prognostic factors have a greater impact on OS than do individual therapies. Patients with adverse risk factors such as elevated beta-2 microglobulin (B2M), plasma cell labeling index, deletions of the long arm of chromosome 13 by metaphase cytogenetics (del 13q) require innovative new treatment strategies. Bortezomib has significant activity in patients with both newly diagnosed and relapsed/refractory MM, but its specific role in patients with adverse features has not yet been defined. Methods: Patients with newly diagnosed “high-risk” myeloma (B2M ≥ 5.5., PCLI ≥ 1, or del 13q) and adequate organ and functional status were eligible. Patients were treated with bortezomib 1.3 mg/m2 day 1, 4, 8, and 11 every 21 days for 8 cycles as induction. After induction, patients were scheduled to receive bortezomib 1.3 mg/m2 every other week indefinitely. Elective peripheral stem cell mobilization (growth factor alone) was allowed after 4 cycles of bortezomib. Patients relapsing on maintenance schedule were to have the full induction schedule resumed. Responses were by the EBMT criteria but a very good partial response category was included. The primary end-point was the response rate in these high-risk patients (90% power to detect a response rate of 50% or higher). The study decision rule requires that 16 or more responses, among 39 eligible patients, are seen in order to declare this treatment effective. Results: Between March 15, 2004 and March 10, 2005, 44 patients enrolled on study. Among the 43 eligible patients, median age was 63; 51% were male. All patients had high risk disease: del 13q (6/41); plasma cell labeling index ≥1% (16/34); and B2M≥5.5 (34/43). Preliminary response data are available for 18 of the 44 cases enrolled, of which 7 had partial response, 1 had minimal response, 1 had no response, 2 had progressive disease, and 5 were unevaluable. Among those patients completing induction therapy and with response information, the median number of cycles of therapy administered is 5, range (0,8). The most common non-hematologic adverse events (AEs) of grade 3 or higher included hyponatremia (9 patients) and diarrhea (6 patients). Mild sensory peripheral neuropathy was common: grade 1, 16 patients; grade 2, 2 patients. Only 1 patient had grade 3 peripheral neuropathy. One patient died after receiving 2 doses bortezomib due to heart block and asystole. Two patients had a grade 4, 25 patients had grade 3, and 13 had grade 1 or 2 as the worst grade non-hematologic adverse event. Based on data received by August 1, 2005, 18 patients have gone off study: AEs (2); death (1); progressive disease (9); and other reasons (6). Updated results on the full study population along with FISH data for IgH translocations and deletions of 13q and 17p will be presented at the meeting. Conclusions: Preliminary results suggest that upfront bortezomib has activity in patients with high-risk MM, but further follow-up is required.

Abstract There is a high background risk of venous thrombotic events (VTE) in patients with Multiple Myeloma (MM) and this is significantly increased following treatment with Thalidomide. The risk may be mediated by the production of a prothrombotic state, via an angiopathic mechanism, or alternatively it may arise from the release of prothrombotic factors from dying myeloma cells targeted by the treatment. No unifying evidence of a prothrombotic state has been demonstrated to date and it remains important to understand the mechanism underlying VTEs in order to prevent them effectively. Inherited genetic variants in the form of SNPs can affect the behaviour of clotting factors. These SNPs are functional and well characterised in the coagulation system and we have used them to analyse the mechanism underlying thalidomide induced VTE. As part of the Bank On A Cure TM project we have carried out an analysis of SNPs in 11 genes: GPIIA, ITGA, MTHFR, MTR, PPARG2, TFP1, VCAM, factor V, HABP2, prothrombin and THDB in a case series of 90 patients treated with thalidomide, with and without DVTs. This study showed a significant association with MTHFR and factor V polymorphisms. In order to validate and extend these initial observations we carried out an analysis of a second nested case control study using 131 Myeloma patients from within the MRC Myeloma IX trial, comprising 47 cases with a VTE and 94 age and sex matched controls exposed to the same treatment. We used a SNPlex panel containing 16 functional venous thrombosis associated SNPs, all of these functional polymorphisms have reported mechanisms and biology. We looked at polymorphisms in the genes: fibrinogen A (Ala331Thr), B (-455G>A, Lys478Arg) and G polypeptides (His140Tyr); Coagulation factors V(Arg534Gln), VIII (Glu1260Asp), IX(Ala194Thr), XI (Phe339Cys) and XII(-4G>T); PROCR(Gly219Ser); prothrombin(19911A>G ,20210G>A); MTHFR(Glu429Ala, Ala222Val); Serpine1(-844G>A) and TFPI(Met292Val). We have typed our selected SNPs using the SNPlex™ Genotyping System, which is based on the oligonucleotide ligation/PCR assay with a universal fluorescently labeled ZipChute™ probe detection. The probes are hybridized to complementary ZipCode™ sequences that are part of genotype-specific amplicons. Probes are eluted and detected on a Applied Biosystems 3130x DNA Analyser. This approach is an attractive alternative to existing genotyping methodologies, as it requires only three unlabeled probes per SNP, and consumes very little gDNA. The data analysis on the polymorphisms frequencies is ongoing to examine associations of thalidomide treatment with VTE events and will be presented. These datasets will allow a better definition of the pathogenesis of thalidomide related VTE, so that we can intervene therapeutically in a rational fashion.

Abstract Background: Multiple myeloma (MM) is a plasma cell malignancy that is incurable with current approaches. The median survival for patients with MM is around four years and a significant proportion of patients experience a course characterized by multiple relapses treated with different therapies. The median survival for patients relapsing after the initial therapy is nearly 18 months and successive treatment strategies result in decreasing response durations, likely reflecting acquired drug resistance. In order to better understand the biological changes associated with advanced, relapsed, refractory MM, we compared gene expression profiles (GEP) of malignant plasma cells isolated from patients with relapsed refractory MM and compared them to plasma cells from patients with newly diagnosed MM. Methods: In order to obtain two relatively homogenous group of patients, we compared samples from 44 patient with newly diagnosed MM enrolled in the ECOG E1A00 clinical trial (comparing thalidomide and dexamethasone to dexamethasone alone) to 44 patients with relapsed refractory MM enrolled in a phase II trial of Velcade (SUMMIT), where most patients had four or more previous relapses. 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: A total of 864 genes were identified which were at least two fold and significantly different between the newly diagnosed and relapsed patients. Using Ingenuity software, 437 of these genes were mapped to different biological networks. Examination of the canonical pathways demonstrated several important cellular pathways differentially regulated between the two groups. Several important mediators of the cytokines, receptors and respective signaling pathways appear to be down regulated in the relapsed group and included IGF-1, HGF, SDF-1 alpha, gp130 and importantly the MEK/ERK pathway. Additionally expression of adhesion molecules such as VCAM1 and PECAM was decreased in the relapsed group compared to newly diagnosed pts. There appear to increased tissue hypoxia in the relapsed marrow as indicated by up regulation of HIF-1 alpha as well increased levels of Placental growth factor. Myeloma cells from relapsed disease were characterized by decreased expression of mcl1, FLIP1, and bcl-xL and increased caspase 8 relative to newly diagnosed group. Also seen was decreased expression of the glucocorticoid and interferon receptors in the relapsed setting. Conclusion: Comparison of the GEP between MM cells from newly diagnosed and relapsed pts demonstrates important differences that have potential biological relevance. The plasma cell in the relapsed setting appears to be more independent of the tumor microenvironment. Additionally, differential expression of some of the genes provides clues to mechanisms of drug resistance commonly observed in the relapsed pts. We are in the process of validating some of the key findings from these analyses.

Abstract H-MM affects about 40–50% of patients, and a similar proportion of MGUS/SMM. The genetic evolution of the disease and the clinical implications of the co-existence of 13q- or t(14q32) are unknown. We conducted a global survey of genetic abnormalities in H-MM in order to describe genetic evolution pathways. H-MM cases from the Mitelman cancer genetic database were combined with cases from the Mayo Clinic MM genetic database. All abnormalities (regional/whole chromosome gains/losses, breakpoints and translocations) were plotted against a 344-chromosome band profile created in a matrix format, and the number of genetic abnormalities per tumor (NAPT) logged. The abnormalities present in more than 5% of tumors were considered recurrent and each karyotype was scored for the presence or absence of each of the recurrent abnormalities. To investigate the temporal pattern of acquisition of genetic abnormalities, all tumors with a given abnormality were selected and the distributions of NAPT plotted. The modes of these distributions were used as a value of ‘lateness’, referred to as time of occurrence (TO). The impact of co-existence of 13q- and t(14q32) on progression free survival (PFS) and overall survival (OS) in H-MM was determined for MM patients entered into an Eastern Cooperative Oncology Group chemotherapy trial. Four hundred and sixty-nine H-MM patients were included in the karyotypic analysis. The median chromosome number was 53 (range 48–74) and the median NAPT was 10 (range 1–25). The distribution of NAPT revealed a normal distribution, suggesting an obligatory acquisition of a given number of genetic abnormalities. The most common abnormalities were trisomies of chromosomes 3,5,7,9,11,15,19 and 21 affecting a third or more of the patients. Deletions and abnormalities of even-numbered chromosomes occur in 20% or less of patients. None of the more than 300 specific translocations detected were recurrent. However, 5 translocation breakpoints were recurrent including 14q32, 8q24 and 11q13. TO analysis suggest that for H-MM, the trisomies are early genetic events, translocations occur midway and deletions tend to be later events. Amongst the trisomies, correlation analysis does not suggest a particular order of acquisition. Clustering analysis using principal component analysis suggests that in H-MM, IgH translocations are secondary events associated with disease evolution rather than defining unique biological entities. Consistent with this, survival of H-MM patients is not affected by 13q- status but is negatively impacted by the presence of t(14q32) especially those with unknown partners (p<0.05). The data presented suggest that H-MM fit a proposed aneuploid model of cancer due to auto-catalytic chromosome reassortment (Duesberg et al Cell Cycle2003:2:202–210). The presence of tumor specific trisomies and the apparent similar phenotype induced by various combinations of trisomies are consistent with this model.

Abstract Background: Thalidomide and dexamethasone combination has been shown to be an effective therapy for multiple myeloma (MM). In newly diagnosed patients with MM, the combination results in overall response rates of over 70% of patients. However, a considerable number of patients do not respond and will need to proceed to other therapies. The ability to identify patients who are unlikely to respond to particular therapies will allow tailoring of therapeutic approaches and in turn will reduce unnecessary toxicity and morbidity from lack of rapid disease control. With the availability of new technology such as gene expression profiling (GEP), this has become a possibility. We employed this approach to identify genes that can reliably predict lack of response to the thalidomide dexamethasone combination. Methods: Patient samples from the Eastern Co-operative Oncology Group clinical trial (E1A00), that compared thalidomide-dexamethasone combination with dexamethasone, and patient samples from the phase II Mayo Clinic study of thalidomide and dexamethasone, both for newly diagnosed MM, were used for this study. Thirty newly diagnosed patients who were evaluable for response were included in the analysis. GEP was performed using the Affymetrix U133A microarray platform as per manufacturer’s recommendation. The Affymetrix output (CEL files) was imported into Genespring 7.2 (Agilent Technologies) microarray analysis software, normalized across chips using GCRMA followed by per gene normalization to median. For the purposes of this study, responses were defined as reduction in the serum paraprotein (or the urinary M protein in the absence of a serum M protein) of >=50% (PR), 25 to 49% (MR), increase of >=50% (PD) and stable disease for the remaining. Results: Five of the 30 patients had no response to the thalidomide Dexamethasone therapy. Using the class prediction tool available in Genespring (Support Vector Machines), we identified 25 genes that reliable predicted non responders (PD, NC) from the responders (MR, PR, and CR). See table for a list of predictive genes with identified genes. Conclusion: Using a combination of two datasets we have identified a set of genes that can be used to reliably predict lack of response to thalidomide and dexamethasone in patients with newly diagnosed MM. We think that this represents a step towards creation of custom microarrays spotted with genes that are capable of predicting lack of response (or response) for the purpose of tailoring therapy to the patient. Gene Predictive strength hypothetical protein FLJ20719 1.655 Tubulin alpha 6 1.653 SEC3-like 1 (S. cerevisiae) 1.649 Ribosomal protein L18 1.546 Ribosomal protein L4 1.432 Tubulin alpha 6 1.409 cleavage stimulation factor 1.393 Tubulin, alpha, ubiquitous 1.393 CD99 antigen 1.38 hmel2 1.346 Leptin receptor 1.291 Integrin-linked kinaseSMC4 structural maintenance of chromosomes 4-like 1 1.269 Cornichon homolog 1.255 Williams-Beuren syndrome chromosome region 5 1.217 Ubiquitin specific protease 1 1.2 Sarcoma antigen NY-SAR-91 1.2

Abstract Bank On A CureTM is a collaborative project initiated with the International Myeloma Foundation to develop a DNA bank through international cooperation. The Bank provides a resource for the development of cooperative partnerships to assess genetic associations with disease etiology and outcomes. It is well established that genetic variation can have a significant ethnic association, and such variations may account for differences in disease incidence and outcomes. Indeed, African Americans have historically shown a 2.5–3 fold increase population incidence of myeloma compared to the Caucasian population. We have identified a number of potentially biologically relevant genetic variants that show significant differences in ethnic distribution. Using both clinical trials as well as the Coriel [normal] cell line data base, we find in all three cases the same highly significant differences (Fishers Exact Test) between African Americans and Caucasians in functionally relevant genetic polymorphisms, as represented by E9486 univariate analysis of IL-6 (p<.00001), LT-a (p<.0001), ERCC312 (p<.0001), XRCC399 (p=.006), IL-1RA (p<.00001), GST-M (p=.0008), and ERCC751 (p<.02). Subsequently, from trial S9321 we have found differences in a number of clinical outcomes associated with ethnicity. When toxicities between Caucasians and African Americans were examined, similar severe toxicity rates were noted after VAD induction related to neuropathies, nausea, and bone pain; whereas statistically significant differences were noted for infection rates, hyperglycemia, fatigue, dyspnea, and thrombosis (p<.04). During high dose melphalan treatment toxicities were not statistically different. Because significant association between IL-6 genotype and ethnicity was noted, we also examined serum levels of sIL-6R and CRP, available from trial E9486. African Americans patients show significantly lower sIL-6R levels compared to Caucasian patients (mean 149 vs 213 ng/ml; p<.0001). Interestingly, there is a significant association between IL-6 SNP genotype and sIL-6R distribution, with the high IL-6 producer GG genotype (in 90% African Americans) having the lowest sIL-6R compared to the CC or CG genotypes (p=.02). Regression analysis of IL-6 SNP and ethnicity on sIL-6R reveals that ethnicity is significantly associated with sIL-6R distribution after adjustment for IL-6 SNP genotype (p=.001) but not vice versa (p=.91), indicating the association of IL-6 SNP genotype with sIL-6R is due to its association with ethnicity. Together the data demonstrate that while associations may result from co-segregation of ethnic genotypes, they may influence disease risk and outcomes related to important genetic factors distributed unequally in different ethnic populations. Such differences may, for example, alter IL-6 responses. Further, multivariate analysis will be developed. Validation of genotype associations found in different ethnic populations will be an important consideration in evaluating myeloma etiology and outcomes.

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 Background: Syndecan-1 (CD138) is a heparan sulfate bearing proteoglycan found on various epithelial cells as well as on B lineage cells depending on its stage of development. Syndecan-1 (CD138) is abundantly expressed by plasma cells, especially myeloma cells. The extra cellular domain along with the heparan sulfate side chains can be cleaved off the cell surface and can be detected in the serum as soluble syndecan. Syndecan possibly plays a multifunctional role in the biology of myeloma. It has been shown to be an independent prognostic factor in patients with multiple myeloma. It has also been shown to promote myeloma cell growth through different mechanisms. Its expression has also been suggested to correlate with bone disease in MM. Methods: In this study we studied serum levels of soluble syndecan in newly diagnosed MM patients enrolled in the Eastern Cooperative Oncology Group (ECOG) E9486 and its associated correlative laboratory clinical trial E9487. We evaluated the prognostic value of syndecan in MM and its relationship to other known prognostic factors for this disease. In addition, syndecan levels were correlated with clinical and laboratory markers of bone disease. Results: A total of 501 patients were studied and the median serum syndecan-1 was 158 ng/mL. Syndecan levels correlated positively with other prognostic factors and markers of tumor burden such as β2-microglobulin (correlation coefficient 0.3; P <0.00001), labeling index (0.25; <0.0001), creatinine (0.23; <0.0001), soluble IL6 receptor (0.3; <0.0001), BM plasma cell percentage (0.16; <0.0006), and disease stage (P=0.0007). Significant differences in the overall and progression free survival was found between two groups of patient separated using the median value as cut-off. The High syndecan group had a median overall survival of 36.3 months compared to 49.3 months for the low syndecan group (P < 0.0001). Similarly, the high syndecan group had progression free survival of 25.4 months compared to 33.5 months for the low syndecan group (P < 0.0001). In a proportional hazards model including syndecan-1 as well as labeling index, β2M, Platelet count, IL-6R, syndecan-1 retained its prognostic value for overall survival (HR 1.3, P = 0.021). Syndecan levels were correlated with various bone markers including C-terminal telopeptide of type I collagen (ICTP), osteocalcin (OC), C-terminal type I procollagen (PICP), bone-specific alkaline phosphatase (BAP), and tartrate resistant alkaline phosphatase (TRAP) and were found to correlate only with ICTP (0.25, P < 0.0001). No correlation was found between clinical markers of bone disease including presence of lytic lesions, osteoporosis and pathologic fractures on X-rays or bone pain. Conclusion: In this large study, we once again confirm the prognostic value of serum syndecan-1 levels in large group of patients with newly diagnosed myeloma. Syndecan-1 level correlates with other disease markers. Syndecan levels also correlated with ICTP, a marker of bone turnover, though no strong correlation was found between syndecan levels and clinical markers of myeloma bone disease. The biological basis of these finding needs further evaluation.